esa-sceva/satcom-chunk-collection huggingface dataset:
# Review on Silver Nanoparticles as a Novel Class of Antibacterial Solutions
## 4 Silver Nanoparticles and Their Antibacterial Role
### Factors Impacting the Antibacterial Activity of Silver Nanoparticles
#### 4.1.2 Nanoparticle Shape  
As presented by [PERSON] et al. [45] the shape also plays an important part in the antibacterial activity. They have analyzed three shapes: spherical, rod-shaped and truncated triangular and their effectiveness against _E. coli_ in solution and agar plates. The study concluded that in terms of biocidal activity truncated triangular nano silver particles ranked the highest followed by spheres and finally rods.  
Observation of the membrane integrity lead to the conclusion that all shape types were able to bond and eventually damage the cell's membrane. However, the truncated triangular shape has the highest number of facets which help with bacterial interaction and increased surface binding, cell uptake and bacteria death [45].  
Other authors have investigated the effect of nano particles' shape against _S. aureus_. They have concluded that nano platelets have the highest toxicity, followed by nano spheres, nano rods and nano cubes [50].  
The study of antibacterial activity is usually conducted in liquid environments in either in vitro or in vivo conditions, therefore the stability (dispersion and chemical - release of ions) of silver nano particles is an issue because it affects its properties and its antibacterial activity as well. When observing the dispersion stability different solutions were used and the aggregation process was monitored using DLS (dynamic light scattering) and UV-vis spectra and TEM (transmission electron microscopy). The authors concluded that SDS (sodium dodecyl sulfate) silver nano particles had the highest antibacterial activity due to their dispensability and cell membrane interaction [45].
# Beneficial Health Potential of Algerian Polysaccharides
## 2 Materials and Methods
### Biological Activities
#### 2.6.2 Anti-inflammatory Activity  
Immunomodulating potential of S-PCPC was evaluated by determining the capacity to inhibit the enzymatic activities of NADPH oxidase (NOX2) and myeloperoxidase (MPO). MPO activity was measured using adapted methods from [PERSON] et al. and [PERSON] et al. (20,21). Briefly, 175 \(\upmu\)L of cell suspension (6 \(\times\) 10\({}^{6}\) cells/mL) were pre-incubatedwith 30 \(\mu\)L of S-PSPC at different concentrations (5-100 \(\mu\)g/mL), 30 \(\mu\)L of different dilutions (5-100 \(\mu\)g/mL) of diclofenac (positive control) or 30 \(\mu\)L of PBS (phosphate buffer saline, negative control) for 10 min at 37 \({}^{\circ}\)C, then stimulated with 25 \(\mu\)L of PMA (phorbol 12-myristate 13-acetate) (10\({}^{-6}\)M) for 10 min at 37 \({}^{\circ}\)C. After centrifugation (320 g, 10 min, 4 \({}^{\circ}\)C), supernatants were incubated with the reaction mixture: 25 \(\mu\)L of 4-AA (4-aminoantipyrine) (1 mg/mL) solution in 0.1M PBS (pH 6.0) supplemented with 100 \(\mu\)L of 0.003% H\({}_{2}\)O\({}_{2}\). The reaction was ended after 5 min by adding 50 \(\mu\)L of 4M H\({}_{2}\)SO\({}_{4}\). The absorbances were measured at \(\lambda\) = 546 nm. NADPH oxidase activity was measured using the method of Boudoukaha which was slightly modified [22]. Briefly, 400 \(\mu\)L of cell suspension (6 \(\times\) 10\({}^{6}\) cells/mL) was incubated with 100 \(\mu\)L of PSPC (5-100 \(\mu\)g/mL), 100 \(\mu\)L of different dilutions (5-100 \(\mu\)g/mL) indomethacin (positive control) or 100 \(\mu\)L of PBS (negative control) for 10 min at 37 \({}^{\circ}\)C. The cells were thus stimulated with 100 \(\mu\)L of PMA solution (10\({}^{-6}\)M), then incubated for 10 min at 37 \({}^{\circ}\)C. For each tube, 100 \(\mu\)L of a freshly prepared Cytochrome C solution (0.2 mg/mL) were added followed by incubation for 15 min at 37 \({}^{\circ}\)C. The absorbance of the supernatants obtained after centrifugation (400 g, 5 min, 4 \({}^{\circ}\)C) were measured at \(\lambda\) = 550 nm. Inhibition activity results were expressed regarding Equation 1 [20]. All the experiments were done in triplicate.  
\[\text{Inhibition }(\%)=(1-(\text{A}_{(\text{sample})})/\text{A}_{(\text{ control})}))\times 100 \tag{1}\]
# Free Space Optics Communication for Ultra-High-Speed Train Running in Evacuated Tube
## 4 Results and Discussion
### Simulation Results  
#### 4.1.1 Divergence Angle  
This section attempts to answer two questions: which divergence angle does have the closest zone to the BS (a dead zone that cannot cover the transceiver with the required divergence radius)? Furthermore, what effect will employing fixed and adaptive beam radius variation with train speed have on received and transmitted power; how will they affect distance along the track (tube)?  
To ensure receiving sufficient power, the nearest point to the BS should have a divergence radius equal to the radius of the transceiver (e.g., dimensions shown in Table 1), to ensure adequate reception. Therefore, Equations (1) and (2) will be used to generate the analysis data.  
Figure 7 shows that the smallest divergence angle that can be used for the proposed design is 6.944 \(\times\) 10\({}^{-5}\) rad. The distance of 550 m with this angle gives 0.05 m divergence at the nearest point between the sender and the transmitter. We cannot use this angle to achieve the requirements of Table 1 (where the beam should cover the receiver diameter of 11 cm), unless the barrier will change the angle and end the transmission at a distance of 1.5 km. Hence, in this case, a higher number of base stations will be required and the time connectivity with each BS will be less compared to other larger divergence angles.  
Figure 8 represents the Gaussian beam distribution diameter for the smallest angle that can be used for the proposed system (6.944 \(\times\) 10\({}^{-5}\)); starting at a distance of 550 m, this distance required for the train speed of 1000 km/h to pass the space to reach the base station in 2 s; at this point (at a distance of 550 m), the diameter of the laser beam is 5 cm, it cannot cover the receiver's aperture in the scenario of Table 1, as it should be 11 cm.  
Furthermore, the figure depicts the diameter at a distance of 1500 (required for the train to cross in 2 s) as 11 cm, so it is appropriate when the train speed is 2700 km/h. In this work, the two scenarios will be analyzed and discussed.  
Figure 7: Gaussian beam diameter versus divergence angles for the proposed method.  
Table 2 indicates the distance of the chosen position for the fixed divergence angle, and it is evident that the angle (6.944 \(\times\) 10\({}^{-5}\)) is appropriate for a length of 1500 m, as well as the diameter of the farthest point (14,200 m) to be one meter (according to the values Table 1, where the beam divergence should cover at least the receiver diameter of 11 cm).  
The distance of the chosen position for the set divergence angles is shown in Table 3. It is clear that the angle (2.0944 \(\times\) 10\({}^{-4}\)) is adequate for a distance of 550 m since the diameter of the Gaussian beam is 11 cm, which is enough to cover the receiver's aperture, and the diameter of Gaussian distribution for the farthest distance is 2.97 m.
# Secure Physical Layer Network Coding versus Secure Network Coding +
## 2 CAF and Secure CAF
### Secure CAF  
Next, we consider the secrecy condition for each message to Receiver \(R\) in addition to the correct decoding. This problem setting is called secure CAF. Here, Receiver \(R\) is required to obtain the modulo sum \(M_{1}\oplus M_{2}\) while the variable \(Y\) in Receiver \(R\)'s hand is required to be independent of \(M_{1}\) and \(M_{2}\). References [7; 8; 9; 10; 11] proposed an approach using lattice codes. Using an efficiently implementable algebraic for CAF given in [29; 30], the reference [21] proposed an efficiently implementable code for secure CAF. (Here, a code is called an algebraic code when the encoding map preserves algebraic operation. For example, Reed Solomon codes and LDPC codes are algebraic codes.) It also showed that the rate \(2I(Y;A_{1}\oplus A_{2})_{\text{Equation}(2)}-I(Y;A_{1},A_{2})_{\text{Equation}(2)}\) is achievable in the BPSK scheme ([21], (29)), where the mutual information is given with the independent and uniform random numbers \(A_{1}\) and \(A_{2}\). That is, when the channel (2) is prepared and Receiver \(R\) colludes with no transmitter, secure CAF guarantees no information leakage of each message to Receiver \(R\) while Receiver \(R\) can recover the sum \(M_{1}\oplus M_{2}\). In the code in [21], \(I(Y;A_{1}\oplus A_{2})_{\text{Equation}(2)}\) is the rate of CAF, and \(I(Y;A_{1},A_{2})_{\text{Equation}(2)}-I(Y;A_{1}\oplus A_{2})_{\text{Equation}(2)}\) is the rate of sacrifice bits for the privacy amplification. Hence, the achievable rate of secure CAF is the difference between these two rates.  
In fact, all the references [7; 8; 9; 10; 11; 21] for secure CAF addressed only the case when the number of transmitters is two. Only reference [37] addresses secure CAF when the number of transmitters is larger than two. Unfortunately, these existing studies proposed no application for secure CAF except for a secure two-way relay channel with untrusted relays. The remaining part of this paper discusses its further application.
# Burnup Credit Evaluation for BWR Spent Fuel from Full Core Calculations
## 3 Models Description
### Full Core Design  
The openly available information about the BWR full core design and operational data is limited to either an outdated reactor configuration, as in [27], or is not presented in all required detail [28]. The recently published study [29] provided insight on an advanced BWR design based on openly available data, including the loading pattern, the operational parameters and the modern BWR GE-14 \(10\times 10\) fuel assembly design. The reactor data from [29] were taken as a basis for the full core analysis.  
The reactor core described in [29] contains 872 fuel assemblies from four different cycles, each having a cycle length of 11.25 GWd/tU, with the layout of a quarter of the core shown in Figure 2. The number of FAs in each cycle is summarised in Table 2, with an identical number of 224 assemblies in the first three cycles and four assemblies less in the last cycle. The main reactor core parameters are represented in Table 3. Thermal-hydraulics data at rated conditions are described in Table 4. Peaking factor estimated for the BWR core without control rods usage has a value around 1.8 [30]. This limitation factor, along with the parameters from Table 4, was used to verify that the developed model is close to the realistic full-core design.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Fuel pellet radius, cm & 0.447 \\ Zr clad radius, cm & 0.513 \\ Fuel rod pitch, cm & 1.295 \\ Assembly pitch, cm & 15.24 \\ Water rod inside radius, cm & 1.1605 \\ Water rod outside radius, cm & 1.2605 \\ Assembly height, cm & 381 \\ \hline \end{tabular}
\end{table}
Table 1: BWR \(10\times 10\) assembly parameters [26].  
\begin{table}
\begin{tabular}{c c} \hline
**Cycle Number** & **FAs Number** \\ \hline
0 & 224 \\
1 & 224 \\
2 & 224 \\
3 & 200 \\ \hline \end{tabular}
\end{table}
Table 2: Fuel assemblies (FAs) number at each cycle.  
Figure 2: Advanced boiling water reactor (ABWR) full-core configuration of one quarter of the core.  
Analysis conducted in [31] for the realistic control blades histories showed that control blade insertion affects the criticality of the storage cask with BWR spent fuel by approximately 0.6-1.2% of the final value. Meanwhile, for the unrealistic histories with massive control rod insertion (92% of irradiation time), the difference was found to be up to 4.3%. Hence, the effect of control rods' movements does not contribute significantly to the criticality of the storage cask as long as the insertion time is limited, as it is assumed in normal, industrial reactor operation, and thus, control rods were omitted in the current study.  
The current full-core simulations do not aim to develop a complete and precise model of the BWR reactor operation but rather a model sufficient to determine the fuel assembly burn-up behaviour in the reactor core. Hence, the evaluation of some safety parameters, such as linear power level in fuel rods [29] or expected transient behaviour, were omitted in the current study.
# Implementing Deep Convolutional Neural Networks for QR Code-Based Printed Source Identification
## 2 Related Work
### Quick Response (QR) Code  
A QR code is a two-dimensional barcode symbol that is categorized as a matrix code. It is made up of black and white squares, known as modules (Figure 1), with the black squares representing a value of 1 and the white squares representing a value of 0.  
There are 40 versions of QR codes, with the smallest being Version 1, which consists of 21 \(\times\) 21 code elements. Each subsequent version increases the number of code elements in the side length by 4. Version 40 has 177 \(\times\) 177 modules, with a total of 31,329 code elements. The largest numbers of character types, data, and error correction levels are defined separately in each session. The QR code system implements the Reed-Solomon error correction algorithm with four error correction levels. Furthermore, the system divides QR codes into disjoint blocks and performs error correction on each block. Each block consists of codewords, and each codeword contains eight data modules. The higher the error correction level, the lower the storage capacity. The approximate error correction capabilities at each of the four levels are as follows:  
* Level L (Low) 7% of codewords can be restored;
* Level M (Medium) 15% of codewords can be restored;
* Level Q (Quartile) 25% of codewords can be restored;
* Level H (High) 30% of codewords can be restored.  
The code elements of a QR code can be divided into non-data areas and data-coding areas. Figure 2 shows the structure of a QR code. The following terms and uses are introduced in the QR code diagram:
# Real-Time Geophysical Applications with Android GNSS Raw Measurements
###### Abstract  
The number of Android devices enabling access to raw GNSS (Global Navigation Satellite System) measurements is rapidly increasing, thanks to the dedicated Google APIs. In this study, the Xiaomi Mi8, the first GNSS dual-frequency smartphone embedded with the Broadcom BCM47755 GNSS chipset, was employed by leveraging the features of L5/E5a observations in addition to the traditional L1/E1 observations. The aim of this paper is to present two different smartphone applications in Geoscience, both based on the variometric approach and able to work in real time. In particular, tests using both VADASE (Variometric Approach for Displacement Analysis Stand-alone Engine) to retrieve the 3D velocity of a stand-alone receiver in real-time, and VARION (Variometric Approach for Real-Time Ionosphere Observations) algorithms, able to reconstruct real-time sTEC (slant total electron content) variations, were carried out. The results demonstrate the contribution that mass-market devices can offer to the geosciences. In detail, the noise level obtained with VADASE in a static scenario--few mm/s for the horizontal components and around 1 cm/s for the vertical component--underlines the possibility, confirmed from kinematic tests, of detecting fast movements such as periodic oscillations caused by earthquakes. VARION results indicate that the noise level can be brought back to that of geodetic receivers, making the Xiaomi Mi8 suitable for real-time ionosphere monitoring.  
Android GNSS raw measurements; Xiaomi Mi 8; GNSS seismology; GNSS ionospheric sounding +
Footnote †: journal: Remote Sensing
## 5 Discussion
### Comparison with Previous Studies
[PERSON] et al. [21] argued that when air temperature (Ta) and environmental factor (EF) are low--i.e., under unfavorable thermal and moisture conditions for vegetation growth--the consistency and correlation between SIF and GPP weaken. Directly using SIF to estimate GPP under such conditions can introduce significant errors. The Qinba Mountain area, situated along the climatic divide between northern and southern China, is intersected by the 800 mm annual precipitation isohyet and the January 0 \({}^{\circ}\)C isotherm, with pronounced elevation variations. As a result, many regions within the area experience suboptimal Ta and EF for vegetation growth, reducing the accuracy of GPP estimation via SIF and introducing uncertainties into carbon sinks calculations. According to the ninth national forest resource inventory in Shaanxi Province [27], the dominant vegetation types in the Qinba Mountain area are coniferous forests, broadleaf forests, and mixed forests. However, a strong linear relationship between SIF and GPP was observed primarily in deciduous broadleaf forests, while this correlation was weak in coniferous and evergreen broadleaf forests. Consequently, applying a universal SIF-GPP linear model across the Qinba Mountain area could lead to substantial estimation errors. The simulation performance of remote sensing ecosystem respiration models also differs across ecosystems [37], and the accuracy of ecosystem respiration values can be influenced by various biotic and abiotic factors, such as ecosystem age and nutrient availability [55]. Given the significant role of the Qinba Mountain area's forest carbon sinks both within China and globally, estimates by [PERSON] et al. [51] suggest that Chinese forests sequester approximately 230 TgC annually, with the forests of the Qinba Mountain area--representing only 9.49% of China's forest area--accounting for over 10.6% of the country's forest carbon during the growing season (June to September). Therefore, using the proportion of forest area to estimate the carbon sinks values for the Qinba Mountain area's forests, as performed in other studies, may lead to underestimations and errors. Comparing the results of this study with those from other major forest regions worldwide (as shown in Table 11), it is clear that the carbon sinks capacity of the Qinba Mountain area forests ranks among the highest globally. Among the forests included in this study, their carbon sinks capacity is second only to that of the forests in Northeast China. In the Amazon rainforest, GPP declines during the rainy season, while the dry season is characterized by frequent wildfires. Combined with the region's vast biomass and high ecosystem respiration rates, these factors contribute to a reduction in overall carbon sequestration [56]. However, due to differences in methodologies and data types used for estimation, significant discrepancies arise, with different researchers often producing varying carbon sinks results for the same forest. This highlights the need for ongoing refinement and improvement of the calculation methods for forest carbon sinks.
## 4 Application of ISSA in Node Location in HWSNs
### Performance Evaluation  
To verify the superiority of ISSA for node localization in HWSNs, we compared it with LS and 15 other meta-heuristic algorithms, including HHO [13], GWO [14], BOA [15], SSA [16], Gold-SA [23], PSO [24], GTO [26], sine cosine algorithm (SCA) [31], DE [32], Archimedes optimization algorithm (AOA) [33], whale optimization algorithm (WOA) [34], elephant herding optimization (EHO) [35], marine predators algorithm (MPA) [36], tu-nicate swarm algorithm (TSA) [37], and Coot optimization algorithm (COOT) [38]. All meta-heuristic algorithms have a population size of 30, an iteration number of 50, and other parameters that are consistent with their original literature. The area of HWSNs is \(100\times 100\) m\({}^{2}\), the number of anchor nodes is 25, and the node communication radius is a  
Figure 5: Impact of heterogeneity of communication radius on network communication: (**a**) original communication radius; (**b**) square communication radius.  
random value within [15; 29], and the normalized root mean square error (NRMSE) is used to evaluate the positioning performance as shown in Equation (13).  
\[NRMSE=\frac{1}{Nu}{\sum\limits_{j=1}^{Nu}}\frac{1}{r_{j}}\sqrt{(x_{j}-\widetilde {x}_{j})^{2}+(y_{j}-\widetilde{y}_{j})^{2}} \tag{13}\]  
where \(Nu\) represents the number of unknown nodes and \((x_{i},y_{i})\), and \((\widetilde{x}_{j},\widetilde{y}_{j})\) are the true and estimated coordinates of the unknown node, respectively.  
The NRMSE and the time required to compute the coordinates of unknown nodes using LS or different meta-heuristic algorithms when all nodes in HWSNs have the same coordinate positions are shown in Table 4. As far as the NRMSE is concerned, the average localization error of LS is higher than that of all other metaheuristic algorithms, reaching 55.57%. This indicates that using meta-heuristic algorithms to solve the coordinates of unknown nodes can indeed reduce the localization error of the nodes. The localization error obtained using different metaheuristic algorithms also varies. The best-performing ISSA has a 13.19% reduction in the average positioning error compared with the worst performing EHO. The top four average positioning errors are ISSA, GTO, SSA, and DE in order, and their average positioning errors are 41.38%, 41.51%, 41.68%, and 41.78%, respectively. Although the NEMSE of the top four meta-heuristic algorithms is not very different, there is a big difference in the positioning time required by each of them.  
In terms of search time, LS takes the shortest time of 0.04 s, while GTO takes the longest time of 10.3 s. The times required for ISSA, GTO, SSA, and DE localization are 1.66 s, 10.3 s, 0.95 s, and 1.47 s, respectively. It can be seen that, when solving the node coordinates of HWSNs, it is difficult to achieve both accuracy and time optimality. However, the localization accuracy is the most critical index in the node localization of HWSNs. Within a certain range, the search time requirement can be reduced appropriately. In summary, the ISSA proposed in this paper is more suitable for node localization in HWSNs.
## 3 Results and Discussion
### Carboxymethylcellulose Characterization
#### 3.1.3 Cytotoxicity Evaluation  
When considering the effects of CMCs on the viability of the selected skin cell lines the results obtained can be seen in Figure 3.  
For both cell lines, the studied CMCs cytotoxicity values were below the 30% threshold (dotted line) defined by the ISO 10993-5:2009 standard [30]. However, while no cytotoxicity was observed, it is interesting to see that for HaCaT cells (Figure 3a) the average metabolic inhibition was 0.14%, while for HDFa (Figure 3b) it was 5.39%. This difference is most likely due to the intrinsic differences between cell lines as fibroblasts are generally described as being more sensitive to compounds than keratinocytes [31; 32; 33].  
The lack of cytotoxicity here observed has been previously described in the literature, as CMC in its various forms has been described as being non-cytotoxic against a variety of cell lines. For instance, CMC hydrogels have been shown to be safe towards human osteosarcoma-derived cells, HaCaT, primary mouse embryonic fibroblast, L929 mouse fibroblasts, and Vero cell line [6; 34; 35; 36; 37; 38]. Similarly, CMC scaffolds for tissue engineering showed no cytotoxicity toward L929 cells, human chondrocytes, and osteosarcoma cells [9; 39; 40; 41] and CMC nanostructures are safe toward fibroblasts, HEK293T, and HaCaT cells [42; 43]. Additionally, anionic polysaccharides, due to their lack of direct interaction with cells, are usually regarded as non-cytotoxic toward human skin [44], as shown by [PERSON] and [PERSON] [45] who demonstrated that a 3% (\(w/v\)) alginate film was biocompatible with human foreskin fibroblasts, and by [PERSON] and [PERSON] [46] who showed that a 2.5 (\(w/v\)) pectin hydrogel had no cytotoxicity towards human neonatal dermal fibroblast.