esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Modeling  
To quantitatively investigate the performance of the ACS-OAR system, detailed analyses are carried out through calculation. In this section, the methodology for performance calculation will be introduced, including the assumptions, parameter definitions, and thermophysical property calculation. The following assumptions are made to simplify the model:  
* The system operates under steady state.
* The heat losses of the devices and pipes are ignored.
* Pressure drops in pipes are neglected.
* Pressures of the absorber and evaporator are the same.
* Solution is saturated at the outlet of the open generator and absorber. Water vapor is saturated at the outlet of the evaporator.
* Heat and mass transfer in the open generator only occur between the liquid and gas phase.
* No condensation occurs in the compression process.  
Figure 3: Schematic of a cryogenic air separation unit coupled with an open-cycle absorption refrigerator.  
As one of the most widely used absorbent solutions for absorption refrigerators, LiBr-H\({}_{2}\)O is selected as the working absorbent in this study. Properties of LiBr-H\({}_{2}\)O are provided by Refprop 9.0. It should be noted that the retrofit of the CASU only takes place in the ACS, and there will not be any change in the operating conditions of the purification and distillation sections.
## 5 Results
### Round-Trip Time (RTT)  
This first stage presents a quantitative analysis of the network latency observed across the three previously defined environments, measured at a distance of 5 m. The experiment consisted of transmitting 1000 ICMP echo request (ping) messages, repeated over three consecutive iterations, yielding a total of 3000 transmitted packets. A fixed inter-packet interval of 5 ms was maintained to ensure consistent temporal spacing between transmissions. Each \"ping\" message had a payload size of 64 bytes.  
The performance assessment was carried out by measuring the RTT between a Wi-Fi client, operating on a desktop computer, and the associated access point, as depicted in Figure 2. The results were independently analysed for each of the three environments considered. Data visualisation was performed using box-and-whisker plots, where the results from all three test iterations were aggregated to enhance clarity. These plots illustrate the maximum (\(RTT_{max}\)), minimum (\(RTT_{min}\)), and mean (\(RTT_{mean}\)) RTT values, with the mean denoted by a red horizontal line.  
To further quantify the consistency of network performance, the mean deviation (_mdev_) was computed. This metric represents the average absolute deviation from the mean RTT value across all test iterations. A lower mdev indicates a more stable environment, where RTT values exhibit minimal variation, suggesting lower levels of interference. Conversely, higher mdev values indicate increased performance fluctuations, likely due to environmental factors such as interference, congestion, or varying channel conditions. Additionally, the dispersion of RTT values, as captured by _mdev_, is proportionally reflected in the size of the box in the box-and-whisker plots, providing a visual representation of performance variability.
## 3 Results and Discussion
### Kinetics Studies  
Kinetics models are important before any further investigation intothe mechanism of adsorption; the most popular are [56; 57] the [PERSON]\({}^{\prime}\)s pseudo-first-order and the pseudo-second-order rate equations. The pseudo-first-order equation is:  
\[\frac{\mathrm{d}\mathrm{q}_{\mathrm{t}}}{\mathrm{d}\mathrm{t}}=\mathrm{k}_{1}. \big{(}\mathrm{q}_{\mathrm{e}}-\mathrm{q}_{\mathrm{t}}\big{)} \tag{7}\]  
where \(\mathrm{q}_{\mathrm{t}}\) (mg/g) is the adsorption capacity at time t (min), \(\mathrm{k}_{1}\) is the rate constant of pseudo-first-order adsorption (min\({}^{-1}\)), and \(\mathrm{q}\)e (mg/g) is the equilibrium adsorption capacity. Integrating Equation (7) for boundary conditions (t = 0, \(\mathrm{q}_{\mathrm{t}}\) = 0 and t = t, \(\mathrm{q}_{\mathrm{t}}\) = \(\mathrm{q}_{\mathrm{t}}\)) leads to the nonlinear equation:  
\[\mathrm{q}_{\mathrm{t}}=\mathrm{q}_{\mathrm{e}^{\prime}}\Big{(}1-\mathrm{e}^{ -\mathrm{k}_{1}.t}\Big{)} \tag{8}\]  
The rate equation for pseudo-second-order kinetics was given as [58]:  
\[\frac{\mathrm{d}\mathrm{q}_{\mathrm{t}}}{\mathrm{d}\mathrm{t}}=\mathrm{k}_{2}. \big{(}\mathrm{q}_{\mathrm{e}}-\mathrm{q}_{\mathrm{t}}\big{)}^{2} \tag{9}\]  
Integrating Equation (9), using the boundary conditions at t = 0, \(\mathrm{q}_{\mathrm{t}}\) = 0 and with the amount of dye adsorbed being \(\mathrm{q}_{\mathrm{t}}\) for any time t, and after rearranging the corresponding rate law, one obtains:  
\[\mathrm{q}_{\mathrm{t}}=\frac{\mathrm{k}_{2}.\mathrm{q}_{\mathrm{e}}^{2}. \mathrm{t}}{1+\mathrm{k}_{2}.\mathrm{q}_{\mathrm{e}}.\mathrm{t}} \tag{10}\]  
where \(\mathrm{k}_{2}\) (g/mg.min) is the pseudo-second-order rate constant. The parameters obtained for the pseudo-second-order model are given in Table 2. At different dye concentrations, the pseudo-first-order model could not describe the entire range of adsorption time (detailed data not shown) and is limited only to the initial time range, as also described by others [57]. However, the pseudo-second-order data treatment presents higher determination coefficient (R\({}^{2}\)) values and lower values for error analysis parameters. Furthermore, the corresponding \(\mathrm{q}_{\mathrm{e(cal)}}\)values are in better agreement with experimental data \(\mathrm{q}_{\mathrm{e(exp)}}\). These results suggested that the pseudo-second-order model can be used to represent our adsorption data kinetics. For each dye, the \(\mathrm{k}_{2}\) value decreases when its concentration increases, and this might be related to higher competition for the adsorption sites when the dye concentration is high [59]. The relative error (ARE) function is a measure of the differences between the experimental amount of adsorbed dye and the value predicted by the model:  
\[\mathrm{ARE}(\%)=\frac{100}{\mathrm{n}}\sum_{\mathrm{i=1}}^{\mathrm{n}}\frac {\left|\mathrm{q}_{\mathrm{e(exp)}}-\mathrm{q}_{\mathrm{e(cal)}}\right|}{ \mathrm{q}_{\mathrm{e(exp)}}} \tag{11}\]  
where n is the number of experiments (here n = 3). The values of ARE (\(\%\)) for the second-order kinetic model are presented in Table 2.
# Energy-Efficient Controller Placement in Software-Defined Satellite-Terrestrial Integrated Network
## 5 Simulation Results
### Comparison with Different Algorithms
The tendency of Figure 8 is similar to Figure 6. When the number of controllers exceeds the optimal range, it brings a further increased burden on the network. Specifically,  
Figure 6: Energy consumption with the different number of controllers in STIN. the average latencies calculated by the Greedy, SA, and MSAP algorithms are identical when there is only one controller. The proposed MSAP algorithm achieves a lower average latency than the other algorithms when multiple controllers are deployed on the STIN. Random and K-means algorithms still do not achieving optimal performance in average latency, and they are unsuitable for solving the proposed EECP problem. We also focus on the average latency between controllers, which represents the average latency for each controller link in the LEO satellite network. It reflects the distribution of controllers in the LEO satellite network. The average latency of different number of controllers is shown in Figure 9. The latency calculated by MSAP, SAP, and Greedy algorithms decreased by nearly 35 ms, 45 ms, and 30 ms, respectively. However, Random and K-means present the tendency to be stochastic, they are not highly related to the number of controllers, and the distribution of controllers is not ruleable. (3) Benefit-to-Cost Ratio (BCR): BCR refers to a proportional reduction with respect to one controller. It is defined as \(BCR=(P_{1}/P_{k})/k,0\leq BCR\leq 1\), where \(P_{k}\) represents the energy consumption when \(k\) controllers are deployed on the LEO satellite network. The higher the value of the BCR, the better the result. Table 4 shows the BCR of energy consumption. When the number of controllers increases, the BCR obtained by the five algorithms shows a decreasing tendency. It needs at least four controllers to reduce the energy consumption to half in the three algorithms. BCR follows the law of diminishing returns. When the number of controllers reaches the capability in the STIN, it will obtain minor benefits in return. The trade-off between cost and energy consumption should be considered while selecting the number of controllers. Figure 7: Energy consumption for controllers in the LEO satellite network. Figure 8: Latency with the different number of controllers in STIN. Figure 9: Latency for each control link in the LEO satellite network.
# Does Organic Farming Increase Raspberry Quality, Aroma and Beneficial Bacterial Biodiversity?
## 2 Materials and Methods  
### Plant Material and Cultivation Method  
Raspberry fruit of the primocane 'Enrosadira' were harvested at the end of September 2019 in two different farms both located in the Cesena area (Emilia-Romagna region,Italy). The organic orchard was cultivated according to European Union (EU) regulation EC 889/2008. An IPM orchard was cultivated according to Agrointesa guidelines which comply with the national regulations (Legislative Decree 50/2012). In the latter orchard, IPM Epik(r), Karate Zeon(r), Laser(r) and Signum(r) were applied for the control of pests and fungal pathogens, using dose and application methods as suggested by the producers. The organic farm did not apply any kind of fertilization, whereas in IPM orchard, plants were fertigated weekly from April to September according to standard practices: calcium nitrate (125 kg/ha), magnesium nitrate (600 kg/ha), ammonium nitrate (100 kg/ha), potassium nitrate (250 kg/ha), potassium phosphate (200 kg/ha), monopotassium phosphate (300 kg/ha), nitric acid (52%, 1000 L/ha), iron (25 kg/ha) and microelements (15 kg/ha) in variable percentages depending on the phenological state.  
To assess fruit quality, aroma and microbiome, fruits were randomly harvested at full ripe stage (i.e., when fruits were easily detached from the receptacle), put in cold boxes and immediately brought to the laboratory. Fruit volatiles and NGS analysis were performed on the same batch of fruits, consisting in three replicates each constituted by a pool of six fruits. The material was kept together for 24 h and subsequently halved for analysis processing.
## 3 Results
### Correlations between Inter-Eye Differences in ONH Parameters and SE/AL  
Inter-eye differences in cup/disc area ratio, horizontal CDR, vertical CDR, and linear CDR were all positively correlated with the inter-eye difference in AL, respectively. A positive correlation was also found between the inter-eye difference in SE and inter-eye difference in cup/disc area ratio, horizontal CDR, vertical CDR, and linear CDR, respectively. Both inter-eye differences in cup area and disc area were significantly correlated with inter-eye differences in AL and SE. The inter-eye difference in rim/disc area ratio was also found to be negatively correlated with the inter-eye differences in AL and SE. No significant correlation was found between the inter-eye difference in rim area and the inter-eye difference in AL/SE. Detailed information is shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **More Myopic Eye** & **Less Myopic Eye** & **\(P\) \#** \\  & **Group (_n = 18_)** & **Group (_n = 18_)** & **\(P\) \#** \\ \hline SE (diopters, mean \(\pm\) SD) & \(-5.15\pm 1.81\) & \(-2.95\pm 1.94\) & \(<\)0.001 \\ Inter-eye difference in SE & & & \\ (diopters, mean \(\pm\) SD) & 2.20 \(\pm\) 0.62 & 2.20 \(\pm\) 0.62 & \\ AL (mm, mean \(\pm\) SD) & 25.37 \(\pm\) 1.38 & 24.52 \(\pm\) 1.29 & \(<\)0.001 \\ Age (years, mean \(\pm\) SD) & 23.63 \(\pm\) 5.25 & 23.63 \(\pm\) 5.25 & \\ Sex (n female/n male) & \(8/10\) & 8/10 & \\ \hline \end{tabular} \# Paired \(t\)-test. AL: axial length; SE: spherical equivalent.  
\end{table}
Table 1: Characteristics of the study subjects.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **More Myopic Eye** & **Less Myopic Eye** & **Mean Inter-Eye** & **\(P\)** \\  & **Group (_n = 18_)** & **Group (_n = 18_)** & **Difference** & **\(P\)** \\ \hline Cup area (mm\({}^{2}\), mean \(\pm\) SD) & 0.37 \(\pm\) 0.30 & 0.52 \(\pm\) 0.32 & 0.14 \(\pm\) 0.26 & 0.032 \\ Cup/disc area ratio (mean \(\pm\) SD) & 0.17 \(\pm\) 0.10 & 0.24 \(\pm\) 0.11 & 0.07 \(\pm\) 0.11 & 0.027 \\ Disc area (mm\({}^{2}\), mean \(\pm\) SD) & 1.96 \(\pm\) 0.52 & 2.07 \(\pm\) 0.57 & 0.11 \(\pm\) 0.34 & 0.194 \\ Horizontal CDR (mean \(\pm\) SD) & 0.45 \(\pm\) 0.16 & 0.55 \(\pm\) 0.13 & 0.10 \(\pm\) 0.17 & 0.033 \\ Rim/disc area ratio (mean \(\pm\) SD) & 0.83 \(\pm\) 0.10 & 0.76 \(\pm\) 0.11 & 0.07 \(\pm\) 0.11 & 0.027 \\ Vertical CDR (mean \(\pm\) SD) & 0.26 \(\pm\) 0.15 & 0.39 \(\pm\) 0.17 & 0.13 \(\pm\) 0.18 & 0.008 \\ Linear CDR (mean \(\pm\) SD) & 0.40 \(\pm\) 0.12 & 0.47 \(\pm\) 0.12 & 0.07 \(\pm\) 0.13 & 0.025 \\ Rim area (mm\({}^{2}\), mean \(\pm\) SD) & 1.59 \(\pm\) 0.32 & 1.55 \(\pm\) 0.40 & 0.04 \(\pm\) 0.22 & 0.505 \\ \hline \end{tabular} \# ONH: optic nerve head. CDR: cup/disc ratio.  
\end{table}
Table 2: Inter-eye comparisons of ONH parameters (paired \(t\)-test).
## 4 Discussion
They found that an attenuation of at least \(-\)53 dB was needed to achieve an interference probability of less than 5%. The results of the study indicate that the RBFNN-predicted models performed better than the GRNN models in terms of protection distance and elevation angle. Specifically, the RBFNN model achieved 100% accuracy in predicting interference, and the curve closer to the desired INR of \(-\)12.2 dB was found to be more effective in blocking interference. The ANN-LMs, including both RBFNN and GRNN, were able to predict interference at various protection distances accurately, and that performance is independent of protection distance. The training performance for both RBFNN and GRNN algorithms was found to be significant, with both models mostly achieving the design goal error of zero with training epochs of 100. Specifically, the significant training performance for RBFNN was approximately 4.17612 \(\times\) 10\({}^{-18}\) for a protection distance of 10 km, 2.81559 \(\times\) 10\({}^{-17}\) for 20 km, and 3.61909 \(\times\) 10\({}^{-18}\) for 35 km. The RBFNN and GRNN models were trained symmetrically until reaching 100 epochs of the same data. These findings suggest that the RBFNN algorithm is a promising approach for accurately predicting interference in wireless communication systems. The paper used ANN-LM algorithms to present an interference analysis between 5G base stations and FSS Earth stations in the 3.4-4.2 GHz frequency band. The results showed that the RBFNN-predicted models outperformed the GRNN models in terms of protection distance and elevation angle. The RBFNN model showed 100% accuracy, and the curve closer to the desired INR of \(-\)12.2 dB was found to be more effective in blocking interference. The ANN-LMs (RBFNN and GRNN) can accurately predict the interference at various protection distances, regardless of the protection distance. The ANN-LM optimisation performance modelling was also carried out to examine in detail the effect of interfering signals on the distance between 5G-BS and FSS-ES. The training results of ANN-LM optimisation performance showed that the present model perfectly reproduced the expression levels of interference in the protection distance between 5G-BS and FSS-ES. The average MSE was maintained at as low a level as possible, and the maximum and average errors were evaluated based on the neuron output values. Finally, the study provided valuable insights into the interference analysis between 5G base stations and FSS Earth stations in the 3.4-4.2 GHz frequency band using ANN-LM algorithms. The RBFNN-predicted models outperformed the GRNN models, and the ANN-LMs (RBFNN and GRNN) can be used to accurately predict the interference at various protection distances. The ANN-LM optimisation performance modelling also provided detailed insights into the effect of interfering signals on the distance between 5G-BS and FSS-ES.
# Rectification for Stitched Images with Deformable Meshes and Residual Networks
## 4 Results
### Quantitative Comparison of Image Rectification
SSIM measures the structural similarity between two images, considering brightness, contrast, and structure. And the SSIM values range from \(-1\) to \(1\), with \(1\) indicating identical images. PSNR compares original and processed images by measuring signal-to-noise strength. Higher PSNR values in decibels (dB) indicate better image quality. MSE evaluates the similarity between images by calculating the difference between pixels, with lower values indicating more similar images. FID primarily assesses dissimilarity between generated and real images in terms of distribution. Lower FID values indicate greater similarity in latent space. An upward arrow in the table indicates that the larger its value, the higher the image quality, and vice versa. produces higher quality results under these blind image quality evaluation metrics. This indicates that the proposed method not only excels in preserving global information but also achieves significant improvements in overall image quality.
# Modelling Electricity Consumption in Cambodia Based on Remote Sensing Night-Light Images
## 2 Study Area and Materials  
### Study Area  
Cambodia is located in the South Central Peninsula of Southeast Asia. The Electricity Authority of Cambodia (EAC) and the Ministry of Mines and Energy of Cambodia (MME) manage its electricity development. In the 21 st century, Cambodia entered a phase of rapid development; the installed power plant capacity increased from 231 MW in 2005 to 3028 MW in 2019, whereas the electricity supply increased from 977 GWh in 2005 to 2015 GWh in 2019, increases of 14.5- and 12-fold, respectively. Indeed, Cambodia's electricity consumption is growing at a much faster rate than that of the rest of the world. This increased access to electricity has significantly contributed to the rapid growth in total electricity consumption. Additionally, there are a large number of areas with lighting, leading to significant growth in both electricity consumption and demand. According to the EAC, unlike most developed countries where the majority of electricity is consumed by industry, Cambodia's significant increase in electricity consumption is related to the provision of lighting in areas where it was previously extremely scarce. This makes it certain that Cambodia's rapid growth in electricity consumption will be well reflected in the total nighttime light data of nighttime images.
## 3 Results and Discussion
### Production and Characterization of XBM  
The production of the XBM using the template chosen through the phase diagram was successfully achieved by the addition of STMP, as a cross-linker, and the incubation of the mixture at 45 \({}^{\circ}\)C for 6 h. From the macroscopic point of view, after stirring the two solutions, the emulsion presented a yellowish color and a homogeneous appearance. Once 5-ASA was added, samples also remained homogeneous, but reddish in tone. It is noteworthy that all formulations did not show phase separation after the incubation period. In addition, the XBM formulations had about a 35% yield, while the formulations containing CaCl\({}_{2}\) showed about a 60% yield. Thus, this parameter was the first evidence of the rapid formation of the microparticles pulling together xylan chains and hindering their diffusion to the continuous phase. Microscopically, the XBM formulation exhibited non-aggregated spherical microparticles (Figure 3A), with a homogeneous particle population distribution, and an average diameter of \(10.7\pm 2.0\) um (Figure 3C).  
The encapsulation efficiency of the 5-ASA was below 4% for the XBM formulation (Table 1). The probable reason for such a low encapsulation rate might be due to the diffusion of the 5-ASA to the continuous phase. This phenomenon might be the consequence of the use of the 1N NaOH on the production of the xylan solution and a long time to achieve the crosslinking process. The alkaline solution increases the pH of the emulsion to approximately 12 and allows the 5-ASA to be soluble on both phases of the emulsion and the crosslinking process lasts for around 6 h. In fact, the 5-ASA is largely soluble into alkaline solutions while its solubility in water is 1.41 mg/mL at 37 \({}^{\circ}\)C [21; 22].  
Figure 2: Phase diagram of xylan and PEG solutions. The solid line (–) represents the binodal line and the dashed lines (- - -) or the tie-lines. Below the binodal lines, the systems have just one phase, and, above it, the open circle (\(\square\)) represents the point at which the phase separation occurs. X\({}_{1}\) stands for the xylan dispersion at 40%(w/v), X\({}_{2}\) is the PEG enriched solution 32%(w/v), and Y\({}_{1}\) and Y\({}_{2}\) represent the volume ratio of the two phases for the formation of the emulsions.
# How 5G Wireless (and Concomitant Technologies) Will Revolutionize Healthcare?
## 5 Case Study: Economic Benefits of 5G-Enabled Healthcare Solutions
### Economic Benefits of IoT  
5G use-cases like the Internet of Things (IoT) will enhance productivity and boost economic activities across the globe. IoT can enable data-driven patient management that will provide improved healthcare effectiveness and efficiencies with minimum cost. IoT will create $14.4 trillion net profit from 2013-2022 by increasing revenues and lowering costs [62]. The following are five factors that fuel IoT value that are at stake:  
Figure 9: Global digital health market from 2013–2020, by segment (in billion U.S. dollars) (source: GSMA Intelligence).  
* Better asset utilization results in a $2.5 trillion cost reduction
* Greater employees' productivity causes a benefit of $2.5 trillion
* Improved supply chain management eliminates waste of $2.7 trillion
* Improved customer experience adding more customers equivalent to $3.7 trillion
* Reduced time to market of $3.0 trillion  
According to the report compiled by McKinsey & Company, IoT will have a total economic impact of $3.9 trillion-$11.1 trillion per year in 2025. In healthcare, it could have an economic impact of 170 billion USD-1.6 trillion per year by 2025. The societal benefits in terms of reduced cost, chronic disease management and improved health of users could be worth more than 500 billion USD per year. These potential annual economic impacts will be 62% in developed economies and the remaining 38% in developing economies. In the past, 3.3 million activity trackers and fitness bands were sold in one year (between April 2013 and March 2014) in the U.S., and these numbers continue to grow [63]. According to Aruba Networks, globally, 60% of healthcare organizations have introduced IoT devices into their facilities, and it is expected that IoT is heading for mass adoption by 2019 with better business results [64]. The productivity gains and cost savings through smart monitoring and adaptive systems are estimated to create $1.1 trillion-$2.5 trillion in the healthcare sector [65]. IoT-based remote patient monitoring will drive the global market at a CAGR of 24.55% by 2020, where the smart pill technologies alone are expected to reach $6.93 billion by 2020 with a CAGR of 23.62% [66].