esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Current Technology
### System User Perception  
After discussing how enabling technologies interact with nutrition and sports, we now turn to one of the critical issues of these systems: their acceptability [43]. Several indicators point to the perceived ease of use and usefulness of a system as leading factors in the introduction of a technology into an existing system. Equally important is the user's attitude toward the technology. If the user perceives a well-defined purpose for the technology within the system, their expectation and desire to implement it will increase, as long as they believe the purpose is effective and aligned with their real goals [44]. To investigate these factors, the technology acceptance model (TAM) has been hypothesized and tested in several fields. Its goal is to understand the gap between system producers and the acceptance of the information technologies involved, specifically studying user perception and perceived ease of use. From 1989 to 2018, the study of the technology acceptance model allowed for the identification of additional factors [45], such as technology anxiety, which is the feeling resulting from the absence or intrusiveness of the technology embedded in the system; technology self-efficacy, which is the feeling of being effective in achieving goals due to a tool embedded in the system; perceived enjoyment; and user satisfaction. Another essential correlation is found between attitude and user satisfaction. When the user perceives strong efficacy of the technology to achieve an upgrade in fulfilling a task of their job before implementation, there is a reduction in user satisfaction at the time of use, reinforcing the distance between the producer and the user. Therefore, finding a solution to reduce the gap between the company and the market and involving the user in system creation phases is essential. Three moments in the relationship between the system and the end-user can be defined based on the factors found so far. The first is the moment before system implementation, in which attitude and intention of use act as facilitators or barriers to the user's approach. The second is the period of actual use of the system, in which technological self-efficacy, technological anxiety, and perceived enjoyment play a role in the user's experience. Finally, the third moment is after use, in which user satisfaction is evaluated and related to the previously mentioned factors.
## 3 Results and Discussion
### Flavonoid Compounds of Rose Petals Based on LC/MS  
In the present investigation, the polyphenic compounds of Iranian rose petals were determined using LC/MS analysis. Table 4 shows the qualitative and quantitative analysis of the rose petals results. The MRM details of the flavonoids are illustrated in Table 2. Consequently, 13 flavonoids were identified, most of which were reported for the first time in rose petals. In terms of the total flavonoid content, the highest was found for the C294 sample followed by the D234 sample, while the lowest content was observed for the A104 sample. The total amounts of flavonoids found in the present study were significantly lower than those found by [PERSON] and [PERSON] [10] in Iranian rose petals. Such differences can occur due to different aspects, such as weather conditions or soil fertility; moreover, there was significant difference in sample preparation, since [PERSON] and [PERSON] [10] extracted flavonoids from fresh rose petals with 80% methanol, assisted by ultrasound, while we have used dried rose petals extracted with pure methanol without ultrasound assistance.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multirow{2}{*}{
\begin{tabular}{c} **Ca-** \\ **Number** \\ \end{tabular} } & \multirow{2}{*}{**RT**} & \multicolumn{6}{c}{**Genotype**} \\ \cline{3-11}  & & & **A1  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & **A104** & **A105** & **B111** & **B213** & **C159** & **C294** & **D231** & **D234** & **D237** \\ \cline{2-10}
Biochanin A (0-1066.89 mg/100 g dw), phloridzin (72.59-375.92 mg/100 g dw) and diosmetin (82.48-153.16 mg/100 g dw) were the most abundant in nine studied cultivars followed by _trans_-chalcone (0-106.29 mg/100 g dw) and diosmin (41.55-84.57 mg/ 100 g dw). More commonly identified in roses and other Rosaceae plants [26; 27], in previous studies naringenin ranged from 3.77 in B111 to 54.70 mg/100 g dw in C294, luteolin ranged from 4.37 in B111 to 28.87 mg/100 g dw in C294, while kaempfer was in the range of 8.56 in B111 to 67.81 mg/100 g dw in C294.  
Previous reports have mostly determined the flavonoids of rose petals [17; 28]. [PERSON] et al. [29] determined kaempfer, quercetin, as important flavonoids in Indian rose petals, which is consistent with our results. However, the present study introduced new flavonoids with their amount in nine studied Iranian cultivars, providing new insights for producing of health benefit products from elite cultivars.
# Distributed Localization of Non-Cooperative Targets in Non-Coplanar Rendezvous Processes
## 1 Introduction
Since the configuration of the three satellites during encounter directly affects the performance of target localization, an improved NSGA-III algorithm, which is based on normal distribution crossover (NDX) and a cosine-like mutation distribution index, was designed to optimize rendezvous configuration. The main contribution of this paper has three aspects: the scheme proposed is applied to the scenario of non-coplanar encounter, which has rarely been studied before; considering the robustness of the system, the DEKF based on CE solves the problem of system collapse when a single-tracker signal is lost; and the configuration optimization during non-coplanar encounter is transformed into a numerical optimization problem, and the NSGA-III algorithm is improved by NDX and the cosine-like mutation distribution index proposed. Compared to traditional algorithms, the improved NSGA-III exhibits a faster convergence rate and yields results that more closely approximate the true Pareto front. The rest of this paper is organized as follows. Section 2 introduces the three-LOS cooperative positioning scheme. The configuration optimization method and algorithm are presented in Section 3. And the numerical simulation and analysis are addressed in Section 4. Finally, the conclusions are given in Section 5.
## 2 Materials and Methods
### CBCT Volume
#### 2.3.2 Thresholding and Skin Segmentation  
Data acquired by CBCT commonly present some degree of noise as well as artefacts. In order to manage the range of voxel values, a thresholding technique was used. Through the aforementioned technique, the image intensity value corresponding to air and noise from different sources was determined. The main area of interest for this study was the skin surface, so smoothing and correcting imperfections represented a considerable challenge, as CBCT shows low contrast for soft tissues.  
The threshold value was determined from the histogram obtained from a scan chosen from our database (Figure 2). In the histogram (logarithmic scale) under consideration, it is possible to observe an absolute minimum within the range of negative values and it was verified that this value could be used to distinguish useful information from noise and air. There is no inconvenience in preserving the data referring to hard tissue, as well as other metallic materials, such as prosthesis, that may be present.  
After defining the range of voxels, the image was binarized to ensure that all voxel values above the threshold (zone of interest) were equal to 1 and the remaining were equal to 0. Therefore, the image of the patient's head becomes a single cohesive volume, facilitating the visualization and data processing.  
Figure 1: Flowchart on the methods used to develop a co-registration method of CBCT and photo images.  
Figure 2: Typical distribution of Hounsfield Unites (HU) values, for a CBCT volume under study. In red, the absolute minimum value of the negative value voxels can be observed. In red is the corresponding threshold value, about \(-634\) HU.
## 2 Materials and Methods  
### Chemicals and Samples Preparation  
Chemicals and solvents were purchased from standard sources. NaReO\({}_{4}\) and vitamin B12 were purchased from Sigma-Aldrich (Buchs, Switzerland). The synthesis of the [Et\({}_{4}\)N]\({}_{2}\)_cis_-[(CO)\({}_{2}\)ReBr\({}_{4}\)] salt (ReFORM-2) and B\({}_{12}\)-ReFORM-2 was performed according to the literature [27; 61]. Glycerol and sodium dithionite (Na\({}_{2}\)S\({}_{2}\)O\({}_{4}\)) were purchased from Reactolab SA (Servion, Switzerland) and Fisher Scientific AG (Reinach, Switzerland), respectively. Diatom samples for FTIR spectromicroscopy imaging and biological studies were prepared by using _Coscindiscus_ and _Aulacoseira_ diatoms, respectively. Diatomaceous earth, in the form of Celatom(r) Fw-14, was obtained from Applied Minerals Ltd. (Burton, Staffordshire, UK). This earth was used to isolate _Aulacoseira_ frustules of average 10 and 23 \(\upmu\)m size, for toxicity studies, according to a published procedure [62]. The silicon skeleton of diatoms is a major component of commercially available filtering agents as, e.g., diatomaceous earth or Celite. _Coscindiscus_ (CF) valves were isolated from Celite 450 (delivered by Sigma-Aldrich, Buchs, Switzerland) according to the following procedure: 1 g of Celite was suspended in 10 mL of 1M HCl (Honey-well Research chemicals, Bucharest, Romania) and stirred vigorously for 15 h at room temperature. The slurry was then filtered and washed with water. The mixture was poured on metallic mesh sieves (300/150/63 \(\upmu\)m) for sequential filtration and washed ten times with 30 mL of pure water. The fraction accumulated on the 63 \(\upmu\)m mesh was collected and dried. Frustules chemisorption of ReFORM-2 and B\({}_{12}\)-ReFORM-2 on _Coscindiscus_ (CF) was achieved by the method described by [PERSON] et al. [63]. Frustules were then filtered and washed several times with dry CH\({}_{2}\)Cl\({}_{2}\) and CH\({}_{3}\)CN (Sigma-Aldrich, Buchs, Switzerland) until washings were clear. Solutions of ReFORM-2 and B\({}_{12}\)-ReFORM-2 are red in color, but after filtration and washing, the visual appearance of the frustules remained that of an off-grey slightly pink material. The loading degrees for ReFORM-2 and B\({}_{12}\)-ReFORM-2 were calculated as 8.0 \(\pm\) 2.2 and 6.8 \(\pm\) 1.7 wt% [63; 64]. These loading percentages are in the normal loading range detailed for other molecules [65; 66; 55]. Isolated _Coscindiscus_ frustules treated by this method are referred to as CF-1 (for ReFORM-2) and CF-12 (for B\({}_{12}\)-ReFORM-2).
# AI-Assisted Security Alert Data Analysis with Imbalanced Learning Methods
## 3 Methodology
### Preprocessing
#### 3.3.4 Data Splitting  
The enterprise and benchmark alert datasets were split into training and test sets, and subsequently, stratified \(k\)-fold cross-validation was applied. Stratified \(k\)-fold cross-validation is an extension of standard \(k\)-fold cross-validation, which ensures that the ratio between the target classes in each fold is the same as that in the original dataset. This method is particularly useful when dealing with imbalanced datasets, such as security alert data, in which the negative class is significantly larger than the positive class. Cross-validation results in more robust models, as it evaluates the performance of the model on unseen data, and it helps reduce the risk of overfitting by providing an estimate of the generalization performance of the model. In addition, it ensures that the model is not overly sensitive to small variations in the training data.
## 2 Data and Methods
### Ensemble Method for TC Precipitation Forecasts  
To comprehensively make use of the precipitation forecasts from different models, i.e., U-Net, UNet3+, SE-UNet, and SE-UNet3+, we use various ensemble methods to enhance the TC precipitation forecast accuracy. The TC precipitation forecast in this study can be considered a quantitative precipitation forecast (QPF). A simple ensemble strategy to improve QPF is to average over all members at each grid point to generate an overall mean (AVR) [47; 48; 49]. This mean value emphasizes the common qualities of individual members while significantly reducing the maximum intensity [50]. Additionally, several studies have demonstrated that the AVR greatly underestimates reported precipitation intensities while increasing the spatial coverage of drizzle [51; 52; 53].  
Another ensemble method to improve QPF outcomes is the probability matching (PM) strategy. [PERSON] (2001) first applied this technique to QPF outcomes [52], assuming that the most likely spatial representation of the rain field is given by the AVR, while the best frequency distribution of rain rates is given by the ensemble of model QFFs. The PM approach has been frequently used to improve the performance of QPF ensembles, outperforming individual QPF members [54; 55; 56; 57].  
The following steps are followed for performing probabilistic matching:  
(1) Compute the ensemble average at each grid point by taking the simple arithmetic average of all members.  
(2) Record the rank and location of each value by sorting the ensemble mean values from lowest to highest.  
(3) Sort the ensemble members' values from lowest to highest and consider every n (n = number of members) values as a group.  
(4) Select the groups from lowest to highest and insert the median of each group into the position where the mean set is sorted from lowest to highest. For example, substitute the grid point with the largest precipitation amount in the ensemble mean with the median of the highest n value in the ensemble member distribution.
## 3 Experiment and Discussion
### Results and Discussion
#### 3.2.3 Response Time for Laser Irradiation  
As mentioned above, the evolution of the temperature field with time varies with different materials and structures. This can also be semi-quantitatively explained by the comprehensive model. As a comparison, Points A (abnormal spot) and B (normal spot, \(x_{\rm A}-x_{\rm B}=100\) um, \(y_{\rm A}-y_{\rm B}=0\) um) in Figure 9, are selected for laser irradiation testing. Several typical bias voltages are selected to test the response of the current variation (Figure 10a,b). The positive or negative value of the current variation will be discussed in Section 3.2.4.  
Due to Equation (12), when the scanning velocity is zero, the temperature variation at the location (\(x=0,y=0\)) is:  
\[\Delta T(0,t)=\frac{(1-R)\,P_{0}}{4\pi\rho c\gamma}\int_{0}^{t}\frac{\mathrm{d }t^{\prime}}{\omega^{2}+4k\,(t-t^{\prime})}=\frac{(1-R)\,P_{0}}{16\pi\kappa \gamma}\ln\left(1+\frac{4 kt}{\omega^{2}}\right) \tag{23}\]  
Although Equation (23) shows that the temperature always increases with laser irradiation time, the increase is very slow after a long time. This is consistent with the pattern shown in Figure 10. Besides, From Equation (20), the current variation is proportional to \(\Delta T\). The response time at the  
Figure 8: Comparison of the current variation \(\Delta I\) in the experiment and temperature variation \(\Delta T\) in the comprehensive model. \(\Delta T\) is calculated through Equation (12) using the data in [16]. (**a**) The laser power varies while the scanning velocity is set at 2500 μm/s. (**b**) The scanning velocity varies while the laser power is set at 40 mW.  
Figure 9: Two selected spots for laser irradiation testing. The laser will continuously irradiate Point A or Point B.  
abnormal spot (less than 100 ms) is much less than that at the normal spot (more than 1 s). This is caused by the different materials and structures in the DUT. The material at the abnormal spot is metal Cu with extremely high thermal conductivity, so \(k=\frac{\kappa}{\rho c}\) is relatively large. However, the material at the normal spot is the insulation material with low \(\kappa\), causing the small \(k\).  
In addition, according to Equation (16), the OBIRCH technique dominates as \(\Delta I>0\), while the SEI dominates as \(\Delta I<0\). Therefore, the OBIRCH and SEI techniques have very similar response times from Figure 10. This conclusion is in accord with the predictions of the comprehensive model also because \(\Delta I\) is proportional to \(\Delta T\).
# Blockchain and Smart Contracts for Insurance: Is the Technology Nature Enough?
## 3 Blockchain Applications in Insurance: Selected Use Cases
### Data Entry/Identity Verification  
The cryptographic mechanism underlying the blockchain could be used to reduce the overhead related to manual data entry and verification of new customers [47].  
With the blockchain, customers would be identified by a unique address (e.g., the one linked to their wallet). The first time they use a service, a certified intermediary would verify their identity and link it to their address. From that time on, every time they undersign a policy, they would no more need to provide an identification document; rather, they would only need to use their credentials.  
Benefits of this use case could be seen again in a reduced time and cost to gather/provide information.  
Nonetheless, this use case also has some relevant drawbacks the company should be aware of. A first drawback is related to the possible loss/steal of credentials. As said, since the blockchain works without intermediaries, no one could reset users' credentials. A solution could be to rely on external services, which could store credentials and return them to the users in case of loss. However, using such services would mean providing someone else access to one's sensitive information. Another drawback is linked to the fact that the current legal regulations should be modified to include blockchain-based identification, and some governments could refuse to approve this type of identification, e.g., due to mistrust in the technology.  
From the architectural point of view, companies deciding to exploit blockchain-based Know Your Customer (KYC) could rely on external services running on public blockchains. In fact, some KYC services recently appeared, offering some prototypes based on existing blockchains. One example is Civic [48] (based on the Bitcoin blockchain) and KYC Legal [49] (exploiting the Ethereum blockchain).  
Such companies already built a network of validators, which receive a reward for each performed validation and charge small fees to companies requiring their services.
# _remote sensing_
## 2 Materials and Methods
### Retrieval of Biophysical and Agoronomical Variables
#### 2.1.1 Coupling EO Data with Crop Models for Yield Prediction  
A large commercial farm in Central Italy was selected for testing SAFY capabilities with data assimilation of EO imagery (Figure 1). The farm is in Macacrese (41.833 lat. N, 12.217 long. E, 8 m a.s.l.), between Rome and the Tirrenian sea. It extends for about 3200 ha of flat area, and grows various annual crops, including winter cereals such as barley, winter wheat and durum wheat. The soil in Macacrese is a Cutanic Luvisol, with a prevailing sandy-clay loam texture. However, based on farmer and researcher experience and on occasional soil sampling, heterogeneous soil conditions have been found in the farm, with more sandy soils in the Southern and Western part, and more clayey soils in the Northern and Eastern part. The area has a typical hot summer Mediterranean climate (Csa in Koppen classification). Long-term average annual precipitation is 812.9 mm, the average minimum temperature in the winter is around 5.0 \({}^{\circ}\)C, and the average maximum in the summer is around 27.4 \({}^{\circ}\)C. The rainiest seasons are autumn and winter, and sowing of winter cereals varies between October and December according to farm management needs and precipitation patterns.
## 3 Results
### Antimicrobial Activity  
The antimicrobial activity of the plant extracts and of their derived AgNPs was tested against three pathogenic microbial strains: a Gram-positive bacterium (_Staphylococcus aureus_), a Gram-negative bacterium (_Pseudomonas aeruginosa_), and a fungal strain (_Candidata albicans_). The results are presented in Table 3 as mean value of the inhibition zone (measured in mm) \(\pm\) standard deviation.  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**EC\({}_{50}\) (ug/mL Final Solution)**} \\ \cline{2-3}  & **Inhibition of Erythrocyte Hemolysis** & **15-LOX Inhibition** \\ \hline Aqueous extract & - & - \\ Ethanolic extract & 53.62 \(\pm\) 1.96 & 75.89 \(\pm\) 6.06 \\ AgNPs-H\({}_{2}\)O & 70.83 \(\pm\) 1.10 & 59.66 \(\pm\) 2.39 \\ AgNPs-EtOH & 280.86 \(\pm\) 0.89 & 79.72 \(\pm\) 3.49 \\ Gallic acid & 44.83 \(\pm\) 0.49 & 13.68 \(\pm\) 0.88 \\ \hline \end{tabular}
\end{table}
Table 2: EC\({}_{50}\) values of _T. eercta_ extracts and of their corresponding AgNPs obtained in the antioxidant assays.  
Figure 16: Graphical representation of the 15-LOX inhibition capacity (%) of extracts and of their corresponding AgNPs solutions, compared to gallic acid.  
The most promising activity on _C. albicans_ of the tested AgNPs in comparison to their corresponding extract was observed for AgNPs-EtOH, with a diameter of the inhibition zone almost double than that of the initial extract (20.0 \(\pm\) 0.00, compared to 12.0 \(\pm\) 0.00 mm), which illustrates the improved antifungal action of the biosynthesized nanoparticles.  
Regarding the testing on _S. aureus_, it can be noticed that the investigated extracts and nanoparticles show a degree of bacterial inhibition capacity (14.0 \(\pm\) 0.00 mm), with no variations between the extracts and the corresponding AgNPs. No activity was detected for any of the tested samples on the Gram-negative bacteria.