esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 CGF
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & **Aim of the Research** & **Results** & **Effect** & **Authors/Year** & **Ref.** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{A-PRF} & & A-PRF might influence bone and & & \\  & & & soft tissue regeneration, & & \\  & & & especially through the presence & & \\  & & & of monocytes/macrophages and & & \\  & & & their growth factors. The & & \\  & & & relevance and feasibility of this & & \\  & & & tissue-engineering concept have & & \\  & & & to be proven through in vivo & & \\  & & & studies. & & \\ \hline \multirow{4}{*}{2} & \multirow{4}{*}{A-PRF} & \multicolumn{2}{c}{To evaluate the potential of} & & \\  & & advanced platelet-rich fibrin & & & \\  & & (A-PRF) as a regenerative & & & \\  & & biomaterial for bone regeneration & & & \\  & & and postoperative sequelae after & & & \\  & & impacted third molar extractions. & & & \\ \hline \multirow{4}{*}{3} & \multirow{4}{*}{A-PRF} & \multicolumn{2}{c}{To evaluate the efficacy and} & & A-PRF has enhanced the healing & & \\  & & healing potential of modified & & & \\  & & formulation of PRF, commonly & & \\  & & known as advanced PRF (A-PRF) & & & \\  & & in impacted mandibular third & & & \\  & & molar extraction sockets. & & & \\  & & & and trismus. & & \\ \hline \multirow{4}{*}{4} & \multirow{4}{*}{A-PRF} & \multicolumn{2}{c}{To evaluate the potential of} & & \\  & & advanced platelet-rich fibrin & & & \\  & & (A-PRF) on bone formation after & & & \\  & & extraction of beagle dogs & & & \\  & & premolars. & & & \\ \hline \multirow{4}{*}{5} & \multirow{4}{*}{A-PRF} & \multicolumn{2}{c}{To investigate and compare the} & \multicolumn{2}{c}{Using of A-PRF after} & & \\  & & postoperative effects of leukocyte & & & \\  & & and platelet-rich fibrin (L-PRF) & & & \\  & & and advanced platelet-rich fibrin & & & \\  & & (A-PRF) in terms of pain, swelling, & & & \\  & & and trismus after mandibular & & & \\  & & third molar surgery. & & & \\ \hline \multirow{4}{*}{6} & \multirow{4}{*}{A-PRF,} & \multicolumn{2}{c}{To investigate the effects of} & & \\  & & concentrated growth factors (CGF) & & & \\  & & and advanced platelet-rich fibrin & & & \\  & & (A-PRF) on edema, pain, and & & & \\  & & trismus after mandibular third & & & \\  & & molar surgery. & & & \\ \hline \multirow{4}{*}{7} & \multirow{4}{*}{CGF} & \multicolumn{2}{c}{To investigate the biological effects} & & & \\  & & of concentrated growth factor on & & & \\  & & human dental pulp stem cells. & & & \\  & & & & \\  & & & & & \\  & & & & \\ \hline \multirow{4}{*}{8} & \multirow{4}{*}{A-PRF,} & \multicolumn{2}{c}{to evaluate the effect of} & & & \\  & & platelet-rich plasma (PRP), & & & \\  & & platelet-rich fibrin (PRF), and & & & \\  & & concentrated growth factor (CGF) & & & \\  & & on bone healing & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the purpose and results of individual authors’ research on A-PRF/CGF; + positive outcome, \(-\) negative outcome, \(+/-\) non-conclusive outcome.
# Range-Free Localization Approaches Based on Intelligent Swarm Optimization for Internet of Things
## 4 Simulation Results and Discussions
### Impact of the Total Number of Nodes  
In this section, our focus is on assessing the accuracy of localization for the proposed techniques within square random, H-shaped, O-shaped, and W-shaped network topologies. We comprehensively analyze their performance by examining the algorithm's ability to respond to changing the number of sensor nodes in the network. In this scenario, the number of sensor nodes ranged from 200 to 500, the communication range remained fixed at 30 m, and the percentage of anchor nodes was set at 20%. The results provide a comprehensive comparison of the proposed localization algorithms based on the number of sensor nodes. Figures 5c-8c depict the simulation results based on the number of nodes. It is essential to highlight that as the density of sensor nodes increases within the region of interest, the network achieves greater interconnectivity. In this scenario, the HW-DV-HopCSO algorithm demonstrates significantly higher localization accuracy, approximately 48%, 47%, 50%, and 43% better than DV-Hop and W-DV-Hop and approximately 34%, 36%, 27%, and 26% better than HW-DV-Hop, PSODV-Hop, and MDV-Hop for random square, H-shaped, O-shaped, and W-shaped network topologies, respectively. Furthermore, in this experiment we observed that our DV-HopCSO, W-DV-HopCSO, and HW-DV-HopCSO algorithms yield better results regarding localization accuracy when contrasted with the competing methods. The HW-DV-HopCSO algorithm exhibits the most outstanding performance among the algorithms under consideration.  
In all considered scenarios, there is a significant improvement in the performance of the HW-DV-HopCSO localization algorithm across the four different network topologies when modifying various simulation parameters. Our results indicate that integrating the CSO method to refine the locations of sensor nodes represents a valuable approach to localization in WSNs.  
Table 3 provides a concise summary comparing the introduced and competing algorithms in terms of minimum, maximum, and mean localization accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Network Topology** & \multicolumn{2}{c}{**Random Square**} & \multicolumn{2}{c}{**H-Shaped**} & \multicolumn{2}{c}{**O-Shaped**} & \multicolumn{2}{c}{**W-Shaped**} \\ \hline
**Algorithm** & **MIN** & **MAX** & **AVG** & **MIN** & **MAX** & **AVG** & **MIN** & **MAX** & **AVG** & **MIN** & **MAX** & **AVG** \\ \hline DV-Hop & 0.032 & 0.693 & 0.282 & 0.021 & 1.065 & 0.379 & 0.053 & 0.806 & 0.282 & 0.017 & 0.772 & 0.274 \\ DV-HopPSO & 0.039 & 1.11 & 0.272 & 0.013 & 1.148 & 0.288 & 0.007 & 0.892 & 0.236 & 0.009 & 0.854 & 0.225 \\ MDV-Hop & 0.025 & 0.647 & 0.258 & 0.022 & 0.974 & 0.327 & 0.025 & 0.522 & 0.208 & 0.031 & 0.79 & 0.299 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation results and analysis for the random square, H-shaped, O-shaped, and W-shaped networks (200 nodes, 40 anchors, R = 30 m).
## 4 Results
### Influence of Urea Crystallization Risk under Operating Conditions
#### 4.1.4 Influence of the Urea Spray Rate on Crystallization Risk  
The variation of maximum urea film thickness with time under different UWS spray rates is shown in Figure 13. The trend of maximum urea film thickness variation under different UWS spray rates is basically consistent and gradually increases with the extension of spray time. At 200 ms ASOI, the maximum urea film thickness at different UWS spray rates has already been between 150\(\sim\)650 \(\upmu\)m. At a high spray rate of 0.32 g/s, the growth trend is most obvious. However, at a low spray rate of 0.16 g/s, the turning point of the stage is not clear. In the interval before the 75 ms ASOI time, there is a small difference in the maximum urea film thickness under each working condition. After the 150 ms ASOI time, there is a difference in the maximum urea film thickness under each working condition. This difference indicates that the UWS spray rate can have a significant impact on crystallization risk.  
Extract the urea film mass accumulation data under different UWS spray rates at 200 ms ASOI time, shown in Figure 14. The accumulation of urea film mass increases with the UWS spray rate and shows a positive correlation. Taking the benchmark operating conditions as a reference, an increase of 0.8 g/s in spray rate results in a 27% increase in urea film accumulation. On the contrary, a decrease of 0.8 g/s in spray rate results in a 30% decrease in urea film accumulation, indicating that the UWS spray rate can significantly affect urea film accumulation. Based on the impact of the UWS spray rate on the maximum  
Figure 12: Variations of urea and UWS film mass accumulation with exhaust flow rates.  
Figure 13: Variations of maximum urea film thickness with time under different UWS spray rates.  
urea film thickness, the UWS spray rate has a significant impact on the crystallization risk of the system. When formulating the spray control strategy for the urea SCR system, it is important to minimize the UWS spray rate as much as possible to reduce the crystallization risk of the system.
# Reduction of Pesticide Use in Fresh-Cut Salad Production through Artificial Intelligence
## 2 Materials and Methods
### Field Tests  
The main goal was to test the prototype in different working conditions considering various operating parameters, to find the most suitable spraying setting in given crop conditions. Two test sessions were carried out, as the pesticides are distributed at different stages of the plants' growth. The height and the width of the salad leaves were taken into account as representative of the crop. In detail, the parameters investigated were:  
* the nozzles' typology: the nozzle type affects the drops' dimension;
* nozzle distance: the distance affects the area covered by the drops, and in particular the spraying overlap between the adjacent nozzles, in order to obtain the maximum distribution uniformity;
* nozzle height: the height of the nozzles from the top of the crop affects the size of the drop-covered area;
* rover speed: the speed affects the amount of mixture distributed on a given surface;
* pressure: the variation of the spraying pressure changes the drop size, affecting the coverage of the crop, but also the possible drift of the mixture.  
A total of 37 tests were carried out, arranging different combinations of the shown features. To check the amount and evaluate the spraying features, 75 water-sensitive papers were used for each test; the sensitive papers were placed in the crop in predefined ways, and after each run collected and analyzed. A depth camera was used to record each test and an image processing algorithm has been implemented to identify the plants and measure height and covered area.  
For the tests, radicchio (_Cichorium intybus_ L.) was selected as the main crop, being representative of most used salads in the ready-to-eat salad sector.
# Assessment of BeiDou-3 and Multi-GNSS Precise Point Positioning Performance
## 5 Data Tests and Results Analysis
### Positioning Accuracy and Convergence Time
#### 5.1.2 Kinematic PPP Results and Analysis
Therefore, increasing redundant observations can increase the reliability of kinematic solutions.
# A Feature-Based Structural Measure: An Image Similarity Measure for Face Recognition
## 3 Similarity Measures
### Overview of SSIM: A Structural Similarity Measure  
Automatic detection of the similarity between images is a significant issue; it plays an essential role in image processing due to its importance in many image processing applications such as enhancement,compression and identity checks. The most important step in the development of image similarity techniques was taken in 2004 by [PERSON] and [PERSON] [9] when they designed the structural similarity measure (SSIM), which considered image distortion as a combination of three factors: correlation, luminance and contrast. This measure is used extensively in image quality assessment and in many image processing algorithms. The basis of this measure is a set of statistical measurements such as mean, variance and co-variance, used to develop a definition for a distance function that can calculate the structural similarity between a test image and training images. The SSIM measure is expressed by the following equation:  
\[\rho(x,y)=\frac{\left(2\mu_{x}\mu_{y}+c_{1}\right)\left(2\sigma_{xy}+c_{2} \right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+c_{1}\right)\left(\sigma_{x}^{2}+\sigma _{y}^{2}+c_{2}\right)}, \tag{1}\]  
where \(\rho(x,y)\) represents the similarity between two images \(x\) and \(y\); \(x\) is the reference image, while \(y\) is usually a corrupted version of \(x\); and \(\mu_{x}\), \(\mu_{y}\), \(\sigma_{x}\) and \(\sigma_{y}\) are the statistical means and variances of images \(x\) and \(y\), respectively. The variable \(\sigma_{xy}\) is the statistical co-variance between the pixels in images \(x\) and \(y\). The constants \(c_{1}\) and \(c_{2}\) are given by \(c_{1}=\left(K_{1}L\right)^{2}\) and \(c_{2}=\left(K_{2}L\right)^{2}\), where \(K_{1}\) and \(K_{2}\) are small constants and \(L=255\) (maximum pixel value). Researchers have confirmed that the values of these constants have little effect on the performance of SSIM [19].
# Spreading-Based Voice Encryption by Means of OVSF Codes
## 2 Orthogonal Variable Spreading Factor Codes  
In Universal Mobile Telecommunications Systems (UMTS), a spreading process is applied in the physical layer. This process involves two operations: Channelization and scrambling. Channelization increases the bandwidth of the signal, transforming each input symbol into a number of chips, being the Spreading Factor (\(SF\)) the number of chips per symbol. Then, a scrambling code is applied to the spread signal [13].  
The channelization codes used in UMTS are known as OVSF (orthogonal variable spreading factor) codes, and its main characteristic is to preserve the orthogonality between different physical channels. To construct OVSF codes, Equations (1)-(3) can be used.  
\[C_{1,0}=[1] \tag{1}\]  
\[\begin{bmatrix}C_{2,0}\\ C_{2,1}\end{bmatrix}=\begin{bmatrix}C_{1,0}&C_{1,0}\\ C_{1,0}&-C_{1,0}\end{bmatrix}=\begin{bmatrix}1&1\\ 1&-1\end{bmatrix} \tag{2}\]\[C_{SF,k}=\begin{bmatrix}C_{2^{n+1},0}\\ C_{2^{n+1},1}\\ C_{2^{n+1},2}\\ C_{2^{n+1},3}\\ \vdots\\ C_{2^{n+1},2^{n+1}-2}\\ C_{2^{n+1},2^{n+1}-1}\end{bmatrix}=\begin{bmatrix}C_{2^{n},0}&C_{2^{n},0}\\ C_{2^{n},0}&-C_{2^{n},0}\\ C_{2^{n},1}&C_{2^{n},1}\\ C_{2^{n},1}&-C_{2^{n},1}\\ \vdots\\ C_{2^{n},2^{n}-1}&C_{2^{n},2^{n}-1}\\ C_{2^{n},2^{n}-1}&-C_{2^{n},2^{n}-1}\end{bmatrix} \tag{3}\]  
Each code is formally specified as \(C_{SF,k}\) where \(SF\) stands for spreading factor, and \(k\) is the code number, \(0\leq k\leq SF-1\).
# Assessing Abrasion Resistance in Concrete Pavements: A Review
## 4 Factors Affecting Abrasion Resistance
### Workability  
The abrasion resistance of concrete was shown to be significantly influenced by the water-cement ratio and the first moist curing of the concrete. It was discovered that concrete's workability affected its abrasion resistance, with overly workable concrete exhibiting noticeably reduced abrasion resistance. Since the porosity of the concrete has a significant impact on its abrasion resistance, it is expected that varying curing conditions will have an impact on this property because the surface moisture of concrete varies substantially. [58,59] Concrete becomes more resilient to stress, abrasion, freezing, and thawing, as well as being stronger and more impermeable with the proper curing [60]. Abrasion resistance during the early stages of concrete curing, or hardening, depends on maintaining the right temperature and moisture content. Insufficient curing might cause more moisture to be lost, making the surface weaker and more prone to abrasion [61].  
Concrete mixes with complete recycled aggregates for all curing conditions exhibit high abrasion resistance, allowing one to conclude that RCA has improved performance in terms of abrasion resistance. The stronger bond formed between the cement and RCA is due to the increased porosity of RCA [62].  
The abrasion resistance of prepackaged aggregate fiber-reinforced concrete (PAFRC) made from leftover polypropylene (PP) fibers was studied, finding that, in comparison to plain concrete, the abrasion resistance of PAFRC was considerably increased by the inclusion of PP fibers (up to 1%). Adding PP fibers improved the resistance to abrasion by about 26%, irrespective of the grouting technique [63]. Segregation can have a detrimental impact on the quality of the hardened state. The concrete's compressive and tensile strengths are significantly diminished when the aggregate is not evenly distributed throughout the mixture. Additionally, the mortar layer that forms on top of the mixture is extremely vulnerable to abrasion forces, indicating that the concrete will likely wear down or flake off very rapidly [64].  
Surface-finishing methods have an influence on the abrasion resistance of concrete. Concrete surface layers with different pore architectures and hardness values are produced by applying various surface-finishing techniques, usually within a few millimeters of the surface. The pore structure and microhardness of the cement matrix at the outermost millimeter have the most effect on the abrasion resistance of concrete built with normal-density particles [65].  
The abrasion resistance of concrete with 6% air entrainment depicts similar abrasion resistance values as regular concrete. With an increase in the air content, a progressive loss in abrasion resistance is seen and is considerably reduced with air entrainment of 10% [13].
## 3 Robustness and Reliability: New Challenges in CMR Segmentation
### Definitions
#### 3.1.1 Reliability  
The ability of a system to perform its required functions under some stated conditions for a specified period of time.
## 4 Results and Discussion
The results of this figure highlight the importance of modulation format and transmission rate to system performance. From the above results, it can be concluded that the hybrid OAM-DM-WDM-OFDM-PON system based on OFDM modulation is more suitable for long-distance and large-capacity downstream transmission compared with other modulation methods.
# Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions
## 3 Results and Discussions
### Evaluation of Performance Metrics
#### 3.2.2 Evaluation Metrics (MAPE, RMSPE, KGE, NSE, R2)  
The comparative performance evaluation of the Artificial Neural Network (ANN) and Random Forest (RF) models is presented through two distinct bar graphs in Figure 10. The first graph focuses on error metrics, specifically Mean Absolute Percentage Error (MAPE) and Root Mean Square Percentage Error (RMSPE), which are crucial for assessing prediction accuracy. The second graph examines efficiency and reliability using Kling-Gupta Efficiency (KGE), Nash-Sutcliffe Efficiency (NSE), and R-squared (R2) metrics, all of which are fundamental for evaluating model performance in predictive applications.  
An analysis of error metrics reveals that the Random Forest model demonstrates marginally better performance than the Neural Network. The ANN shows an MAPE of 3.6727% compared to RF's 4.3733%, while, for RMSPE, the ANN records 11.4334% versus RF's 14.1956%. These differences, though relatively small, suggest that RF may have a slight advantage in minimizing prediction errors for this application. However, when examining the efficiency metrics, the Neural Network clearly outperforms Random Forest, achieving near-perfect scores of approximately one for all three measures (KGE, NSE, and R2). This indicates superior overall model efficiency and stronger correlation with the observed data.  
Further validation using the FUHEM Madrid dataset confirms the ANN's superior predictive capability. The model achieves an exceptionally low MAPE of 0.0272% compared to RF's 0.5426%, demonstrating remarkable accuracy. Similarly, the ANN attains a perfect NSE score of 1, while RF scores 0.9. These consistent results across multiple evaluation  
Figure 10: Performance of two machine learning models: (**a**) (LUCIA Building, nZEB-HVAC), (**b**) (FUHEM Building-Heating).  
metrics strongly suggest that the Neural Network is the more effective choice for HVAC demand prediction in this context, particularly when considering both accuracy and model efficiency. A comprehensive comparison provides valuable insights for model selection based on specific performance requirements and application needs.
# Screening of Plants and Indigenous Bacteria to Improve Arsenic Phytoextraction
## 3 Results and Discussion
### Phytotoxicity Test  
The phytotoxicity test was considered as a preliminary investigation to evaluate plant growth and provide information to support further investigations performed in this study.  
As shown in Figure 3, GI% and Inh% data for soils S2 (55% \(\pm\) 5.8% and 34% \(\pm\) 5.5%, respectively) and SW (77% \(\pm\) 4.9% and 15% \(\pm\) 5.9%, respectively) suggest the lack of adverse conditions for plant growth. In fact, high GI% and low Inh% values are indicative of limited phytotoxicity. The results confirmed the low bioavailability values of As, which did not cause significant toxic effects in the species used. To a lesser extent, a similar consideration can also be made for soil S6, which, although starting from non-optimal values, does not prevent the growth of plants, as also highlighted by the general increase in GI% values and the reduction of Inh% following the different treatments. Conversely, this does not apply to PZ soil, where only corn has managed to germinate and grow, albeit with greater difficulty than in the other soils.  
An improvement in phytotoxicity indices was generally observed in all soils after treatment with phosphate and the addition of PGPR bacteria, but phytotoxicity indices did not improve in the case of PZ soil, in line with the fact that this is the soil for which phosphate extraction was found to have the greatest efficacy. In the other soils, since the quantity of As released by the treatment with phosphate is very low, the positive effects of the mobilizing agent and of the PGPR bacteria appear to be predominant.