esa-sceva/satcom-chunk-collection huggingface dataset:
# Chemical and Sensorial Characterization of Scented and Non-Scented Alstroemeria Hybrids
## 3 Results  
### Sensorial Confirmation of Scented Hybrids  
In the sensorial analysis the hybrids 13M07 and 14E07 were the two genotypes assessed as consistently scented, in comparison with other hybrids where scent was perceived rarely or as absent by the evaluators. Both hybrids come from the cross between the native species _A. pelegrina_ and the scented _A. carophyllaea_ line 0276-02 (Figure 1). On the other hand, the hybrid 13B01, despite having a scented line as parental line (UC-05) (Figure 1), was consistently assessed as 'non-scented' by the evaluators and was used as a negative control.  
Interactions between the factors evaluated were statistically significant. Thus, there was an effect of the stage of development and the time of evaluation on the perception of floral scent. While the hybrid 13M07 was fully perceived as scented (>0.5) in all the stages of development and all the times of evaluation, 14E07 was mainly perceived as scented at stages S4 and S5 and during the afternoon (1:00 p.m. and 4:00 p.m.). On the other hand, the hybrid 13B01 was consistently perceived as 'non-scented', except when the sensorial analysis was performed at 4:00 p.m., when only a few evaluators considered this hybrid as scented (Table 1).  
Floral scent intensity was evaluated as higher for the hybrid 14E07, with an average value of 7.11 ('moderately high'), in comparison with hybrid 13M07 that showed a mean value of 3.58 ('slightly low') (Table 1).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Hybrid**} & \multicolumn{3}{c}{**Stage of Development**} & \multicolumn{3}{c}{**Time of Evaluation (h)**} & \multirow{2}{*}{**Intensity**} \\ \cline{2-3} \cline{5-8}  & **S3** & **S4** & **S5** & **10:00 a.m.** & **1:00 p.m.** & **4:00 p.m.** & \\ \hline
13M07 & 0.50 & 0.68 & 0.68 & 0.50 & 0.61 & 0.75 & 3.58  \\
14E07 & 0.00 & 1.00 & 0.68 & 0.00 & 0.50 & 1.00 & 7.11  \\
13B01 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.27 & ni ** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the averages obtained for the sensorial analysis of floral scent perceived in alstroemeria (yes = 1 and no = 0) at different stages of development and different times of evaluation. Values for the intensity of the floral scent (extremely high = 9 and extremely low = 1) are shown for the scented hybrids.  
Figure 1: Scented (13M07 and 14E07) and non-scented (13B01) alstroemeria hybrids evaluated, showing their pedigree and floral morphology at stage S4 [26]. A scale bar (=1 cm) is included on the left upper corner of each picture.
## 2 Materials and Methods
### MTT Cytotoxicity Assay  
The present study obtained three different human cell lines, namely embryonic kidney (HEK293), breast adenocarcinoma (MCF-7), and cervical carcinoma (HeLa), from the ATCC located in Manassas, VA, USA. These cell lines were cultured in 25 cm\({}^{2}\) tissue culture flasks until they reached confluence. The culture medium used for this purpose was Eagle's Minimum Essential Medium (EMEMEM), supplemented with 10% (\(v/v\)) gamma-irradiated FBS and 1% Penicillin-Streptomycin mixture. The cells were incubated in a HEPA Class 100 Steri-Cult CO\({}_{2}\) incubator manufactured by Thermo-electron Corporation, USA, at a temperature of 37 \({}^{\circ}\)C in an atmosphere containing 5% CO\({}_{2}\). Upon reaching confluence, the cells were trypsinized, transferred to clear 96-well plates, and incubated at 37 \({}^{\circ}\)C for 24 h to promote cell adhesion. Subsequently, the cells were provided with fresh complete growth medium (EMEM + 10% FBS + 1% Penicillin-Streptomycin mixture). To conduct cell treatment, different concentrations of latex AgNP samples, approximately 100 \(\mu\)L, were added to the cells, followed by incubation at 37 \({}^{\circ}\)C for 48 h. Control cells that were not subjected to any treatment were utilized as the affirmative control, showcasing a viability of approximately 100%. The entirety of the experiments was performed in triplicate.  
The methods briefly described by [PERSON] [26] and [PERSON] and [PERSON] [27], were used to assess the cytotoxic potential activity of the latex-synthesized AgNPs on the cell lines. After 48 h of incubation at 37 \({}^{\circ}\)C, the growth medium was aspirated and replaced with 100 \(\mu\)L of medium containing 10 \(\mu\)L of MTT solution (5 mg/mL in PBS), and the cells containing the treatment and negative control were incubated for a further 4 h at 37 \({}^{\circ}\)C in 5% CO\({}_{2}\). The medium-MTT mixture was then removed and replaced with 100 \(\mu\)L dimethylsulphoxide (DMSO). The absorbances were then determined at 570 nm using a Mindray MR-96A microplate reader (Vacutec, Hamburg, Germany). Graphs were used to determine the concentration at which 50% cell growth inhibition (IC\({}_{50}\)) occurred. Percentage cell survival was calculated following the equation below.  
\[\%\ \text{Cell survival}=\frac{\left(\text{Average optical density of control cells only}\right)}{\left(\text{Average optical density of treated cells}\right)}\times 100\]
# Suitability of Aerial Photogrammetry for Dump Documentation and Volume Determination in Large Areas
## 5 Conclusions  
The presented validation study aimed to compare the modern geodetic technologies TLS and AP for determining the volume of heaps of material and their spatial documentation. In addition, the GNSS RTK method and TLS were also compared to bind actual measurements with previously used surveying methods. As the reference method, TLS was chosen because of the high density, the number of measured points, and the high measuring accuracy.  
AP measurements are very fast and can cover a large area in a short time and from a high flight altitude (even compared to UAVs). The time required for fieldwork in comparison with conventional surveying techniques is markedly shorter. The method is therefore also suitable when dynamic change detection in a short time is needed. The measurement is non-contact, and thus it can be safely implemented even in harmful or hazardous environments.  
Conceptualization, L.K., P.B., R.U. and M.S.; methodology, L.K., P.B., R.U. and M.S.; software, L.K.; validation, R.U., M.S. and M.B.; formal analysis, M.B.; investigation, L.K., P.B.; resources, L.K., P.B., R.U. and M.S.; data curation, R.U. and M.S.; writing--original draft preparation, L.K. and M.S.; writing--review and editing, L.K., P.B., R.U. and M.S.; visualization, L.K. and M.S.; supervision, L.K. and M.S.; project administration, L.K., P.B., R.U. and M.S.; funding acquisition, M.B., L.K. and P.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the grant 1/0588/21 funded by the Scientific Grant Agency of The Ministry of Education, Science, Research and Sport of the Slovak Republic (VEGA); APVV-18-0351 funded by the Scientific Grant Agency of The Ministry of Education, Science, Research and Sport of the Slovak Republic (APVV); grants No. 004 TUKE-4/2019 and 055 TUKE-4/2021 funded by the Cultural and Educational Agency of The Ministry of Education, Science, Research and Sport of the Slovak Republic (KEGA); and the grant SGS21/053/OHK1/1T/11 funded by CTU Prague, Czech Republic.  
Not applicable.  
To Geodeticca Vision company for providing material and support to this work.  
The authors declare no conflict of interest.  
References
## 3 Network Model  
Our network model in Figure 2 consists of three types of nodes: a gateway, sinks, and sensors in a Wireless Body Sensor Network (WBSN) for smart healthcare. We can use a unidirectional connected graph to represent the network topology. Here we use \(G=(V,E)\) to indicate backbone network topology of a WBSN, where \(E\) refers to the set of wireless links, and \(V\) refers to the set of network elements, including a gateway, sinks, and sensor nodes. Network traffic generated from rock-bottom sensors will converge the wireless gateway \(T\). The gateway can be connected to the external wired Internet. In our network model, we mainly investigate a many-to-one traffic model from various sensor nodes to an external gateway. Depending on the number of hops to the gateway node, sink nodes are layered in many levels. It is assumed that nodes in WBSNs are equipped with multi-interface multi-channel functions. In this way, every network element can exchange information with its neighbours at the same time. We assume that every sensor user \(U_{k}\) must be connected to at least two sink nodes \(L_{k}\) for connectivity. We indicate all sensor elements by \(U_{s}\), in which \(U_{s}=\{S_{1},S_{2},...,S_{n}\},U_{s}\subset V,|U_{s}|=n\). We assume the min-cut between \(L_{s}\) and \(T\) to be \(h\). This assumption means that the degree of connection between sink nodes and gateway nodes is very rich. It is assumed that all native or encoding packets are the same size for encoding efficiency. It is assumed that gateways can perform a decoding operation to release original information. According to the well-known Gauss Elimination (GE) method [28], destination nodes have a higher probability of recovering their desired packets by solving groups of linear equations, if the information they receive is sufficient for GE solutions.  
Figure 2: Network model.
## 3 Results
### Spectral Analysis  
* Coherence  
The CoP-target coherence values ranged between 0.75 and 0.99 for all participants under all tracking conditions (Figure 3A), suggesting that all participants were successful in tracking the target. However, CoP-target coherence decreased significantly with chaotic tracking relative to the periodic target (F(1,14) = 264, \(p\) = 0.000, h\({}^{2}\) = 0.950), although this was not different between the two feedback conditions.  
* Phase  
The CoP-target phase lag significantly increased when tracking the chaotic compared to the periodic target (F (1,14) = 67.19, \(p\) = 0.000, h\({}^{2}\) = 0.828). A negative phase lag value indicated that the CoP followed the target motion (Figure 3B). A significant feedback by target interaction effect on the phase lag (F (1,14) = 8.51, \(p\) = 0.011, h\({}^{2}\) = 0.378) suggested that the effect of feedback was dependent on the target motion. Post hoc analysis confirmed that the presence of visual feedback significantly increased the CoP-target phase lag compared to the no feedback condition only in chaotic target tracking (t (16) = 2.93, \(p\) = 0.010). On the other hand, the feedback signal had a non-significant effect on the CoP-target phase lag when tracking the periodic target motion.  
* Gain  
Removal of feedback resulted in a significant increase in the CoP-target gain (F (1,14) = 68.49, \(p\) = 0.000, h\({}^{2}\) = 0.830), suggesting that target overshooting occurred during both periodic and chaotic  
Figure 2: Group-averaged (\(n\) = 15) CoP and target (black) time series during performance of (**1 st row**) the sinusoidal and (**2 nd row**) the chaotic tracking with (blue) and without (red) feedback. Dotted lines indicate the group 95% confidence intervals.  
target tracking (Figure 3C). More target overshooting was observed when tracking the chaotic target compared to the periodic target (F (1,14) = 77.57, \(p\) = 0.000, h\({}^{2}\) = 0.847). The impact of visual feedback on CoP-target gain was significantly greater when tracking the chaotic compared to the periodic target, as confirmed by a significant feedback by target interaction effect (F (1,14) = 22.16, \(p\) = 0.000, h\({}^{2}\) = 0.613). Specifically, the increase in CoP-target gain induced by the removal of feedback was greater when tracking the chaotic compared to the sinusoidal target (t (16) = 7.81, \(p\) = 0.000).
## 4 Steady-State Performance Index and Optimal Gains
### Derivation of Smoothing Performance Index  
The smoothing performance index is derived using (35). We assume that \(x_{\text{o},k}\) contains noise with variance \(B_{\text{x}}\) and the target moves with a constant jerk because the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter uses the constant-jerk motion model, as indicated in (4). Thus, the true motion assumed in (35) is  
\[x_{\text{ts},k}=x_{\text{ts},k-1}+Tv_{\text{ts},k-1}+\frac{T^{2}}{2}a_{\text{ ts},k-1}+\frac{T^{3}}{6}j_{\text{ts},k-1}, \tag{44}\]  
where \(v_{\text{ts},k}\), \(a_{\text{ts},k}\), and \(j_{\text{ts},k}\) are the true velocity, acceleration, and jerk of the target, respectively, used for deriving the smoothing performance index. By calculating (35) using definitions (1)-(8) and (44), the smoothing performance index of the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter is derived as:  
\[\sigma^{2}_{\text{p},\alpha\beta\gamma\delta}(\alpha,\beta,\gamma,\delta)= \frac{g_{1}(\alpha,\beta,\gamma,\delta)}{g_{2}(\alpha,\beta,\gamma,\delta)}B_ {\text{x}}, \tag{45}\]  
where  
\[g_{1}(\alpha,\beta,\gamma,\delta) =-3\alpha\gamma^{2}(\delta-12\beta-24\alpha+48)-((\alpha+2) \delta^{2}+(-6(\alpha+2)\beta-24\alpha^{2}+144)\delta\] \[\quad-72\beta^{2}(\alpha+2)-144\alpha^{2}\beta)\gamma_{\gamma}+6 \alpha\delta^{2}(\alpha+2)-(72\alpha\beta(\alpha+2)+144\alpha^{3})\delta, \tag{46}\] \[g_{2}(\alpha,\beta,\gamma,\delta) =(\delta-12\beta-24\alpha+48)(3(\alpha-2)\gamma^{2}+\alpha\gamma (\delta+6\beta)-6\alpha^{2}\delta), \tag{47}\]  
The derivation of \(\sigma^{2}_{\text{p},\alpha\beta\gamma\delta}\) is given in Appendix D.
# A Novel Dual-Wavelength Method for Evaluating Temperature Effect in Fiber-Optic SPR Sensors
The proof-of-concept experiment arrangement is as simple and robust as the conventional single-channel fiber-optic SPR sensor. The experimental results demonstrate that the presented dual-wavelength method is feasible in technology and can be used in practical applications.
# QFC: A Parallel Software Tool for Feature Construction, Based on Grammatical Evolution
## 5 Conclusions  
A feature construction method and the accompanying software were analyzed in detail in this paper. The software is developed in ANSI C++ and is freely available on the internet. The proposed technique constructs technical features from the existing ones by exploiting the possible functional dependencies between the features, but also the possibility that some of the original features do not contribute anything to the learning. The method does not require any prior knowledge of the objective problem and can be applied without any change to both regression and classification problems. The method is divided into two phases: in the first phase, a genetic algorithm using grammatical evolution is used to construct new features from the original ones. These features are evaluated for their accuracy with some machine learning model. However, this process can be very time consuming and as a consequence, fast learning models or parallel algorithms should be used. Radial basis networks were used in the first phase of the method during the experiments, which are known to have short training times, but other models could be used in their place. In the second phase of the method after the features are generated, a machine learning method is applied to them and the error on the test set is evaluated.  
The user can choose between several learning models and can customize the course of the technique through a series of command line parameters. From the extensive execution of experiments and the comparison with other learning methods, the superiority of the proposed technique and its ability to achieve high learning rates even with a limited number of constructed features or a maximum number of iterations emerge. These results combined with the ability of the method to run on multiple threads through the OpenMP library make it ideal for learning large sets of data in a satisfactory execution time.  
The method can be extended in several ways, such as:  
1. Incorporation of advanced stopping rules.
2. Usage of more advanced learning models such as SVM.
3. Addition of more input formats such as the ARFF format or CSV format.
4. Incorporation of the MPI library [99] for a large network of computers.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not Applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The experiments of this research work were performed at the high-performance computing system established at Knowledge and Intelligent Computing Laboratory, Dept of Informatics and Telecommunications, University of Ioannina, acquired with the project \"Educational Laboratory equipment of TEI of Epirus\" with MIS 5007094 funded by the Operational Programme \"Epirus\" 2014-2020, by ERDF and national finds.  
**Conflicts of Interest:** The author declares no conflict of interest.
## 3 Materials and Methods
### Feature Selection
Therefore, not only is it desirable to maximize the relevance of the variables with the class, but it is also desirable to minimize the redundancy between them. To that end, the minimum redundancy criterion is defined as:  
\[minR(S),R=\frac{1}{|S|^{2}}\sum_{v_{i}\in S}I(v_{i};v_{j}) \tag{9}\]The minimal redundancy maximal relevance (mRMR) algorithm combines Equations (8) and (9) according to the following expression:  
\[ \begin{array}{l}max\Psi(D,R),\Psi=D-R;(MID)\\ max\Omega(D,R),\Omega=D/R;(MIQ)\end{array}  \tag{10}\]  
_M1D_ stands for Mutual Information Different criteria, and the other possible implementation for the mRMR algorithm is the Mutual Information Quotient (_M1Q_). In this contribution, _M1D_ is used. The authors of the algorithm [49] suggest an iterative method to find a set of variables close to the optimum since, in practice, it is unfeasible to try to evaluate the Equation (10) for all possible subsets of \(m\) variables in the original space. Assuming the suboptimal set \(S_{m-1}\) of \(m-1\) variables to be found, the following variable to be added will be the one that verifies:  
\[\underset{v_{j}\in X/S_{m-1}}{max}\left\{I(v_{j};c)-\frac{1}{m-1}\sum_{v_{j}\in S _{m-1}}I(v_{j};v_{l})\right\} \tag{11}\]  
In the first iteration of the algorithm, the redundancy term is null, so the first variable selected by mRMR is the same as the one chosen by MaxDep (the one that has the maximum relevance with the class). The 3D-DWT transform produces a large number of coefficients (thousands), making it necessary to select them using mRMR (the 30 most relevant are selected). It should be kept in mind that reducing the number of coefficients for each region of interest reduces the computational complexity and memory size of the matrix of coefficients used. It should be noted that since there are 550 MRI images for the training phase, for each image, 1728 possible VOIs are obtained, each VOI has 30 coefficients and both whole and gray matter are treated, the total number of coefficients that it is necessary to store is 57,024,000.
## 3 Results and Discussion
### Optimization of IL Concentration  
The effect of the [C\({}_{\textrm{s}}\)mim]Br solution concentration (from 0.25 to 1.25 M) on the yields of target analytes was studied. The results are shown in Figure 4c. It can be clearly seen that the yields of CPT, HCPT, VCS-LT, and EOs increased greatly with increasing concentration from 0.25 to 0.75 M. This may be because the amount of dissolved cellulose increased with increasing of [C\({}_{\textrm{s}}\)mim]Br concentration; this would enhance the penetrability of cell walls resulting in enhancement of the extraction capacity of the solvent. A higher IL concentration also enables efficient absorption and transfer of microwave energy [48], therefore microwave absorption and microwave conversion both increase, resulting in increased yields of CPT, HCPT, VCS-LT, and EOs. However, with further increases in the [C\({}_{\textrm{s}}\)mim]Br solution from 0.75 to 1.25 M, slight decreases in the yields of CPT, HCPT, VCS-LT, and EOs were observed. This may be because the increasing viscosity of the IL solution with increasing concentration resulted in poor permeability and diffusion, making it difficult for the IL to infiltrate into the plant tissue, leading to lower yields of the target compounds. Based on all the above results, 0.75 M [C\({}_{\textrm{s}}\)mim]Br was identified as the most appropriate solvent for use in subsequent experiments.
## 2 Materials and Methods
### Drop Size Distribution Modelling  
Drop size can be described by means of a suitable probability density function (PDF) of the number or the volume of the drops in function of the diameter. In a spray consisting of \(N_{T}\) drops, denoting the PDF of the drop number with \(f_{0}(D)\), the quantity \(N_{T}f_{0}(D)dD\) represents the number of drops whose diameter is between \(D\) and \((D+dD)\). Several PDFs have been proposed in the spray literature to describe the drop size distribution, among which the normal, the log-normal, the Rosin-Rammler, the Weibull, and the gamma [45; 46; 47]. In this study, the experimental data were analyzed, and the theoretical distribution, from those mentioned above, that best described them was devised, adjusting its parameters according to the operating conditions. The theoretical study of the atomization process as a function of nozzle characteristics, liquid properties, and test conditions is outside the scope of the research.  
Based on skewness and kurtosis values of measured drop diameters, the theoretical distribution most suitable for fitting experimental data was the gamma distribution. The probability density function of the gamma distribution has the general form of Equation (10)  
\[f(x)=\frac{1}{\beta^{\alpha}\Gamma(\alpha)}x^{\alpha-1}e^{-x/\beta} \tag{10}\]  
defined for \(x\geq 0\), \(\alpha>0\), and \(\beta>0\); \(\alpha\) is the shape parameter, \(\beta\) is the scale parameter, and \(\Gamma(z)\) is the gamma function, defined as  
\[\Gamma(z)=\int_{0}^{\infty}t^{z-1}e^{-t}dt \tag{11}\]  
Both shape and scale parameters affect the probability density and the cumulative distribution function (CDF) as shown in Figures 4 and 5.  
In this study, the shape and scale parameters were estimated for each nozzle and working pressure by using the R package _fitdistrplus_ ver 1.1-5 [48] and applying the Maximum Likelihood Estimation (MLE) method. In addition, their confidence interval was computed based on the bootstrap method. Goodness of fit was assessed by means of the Anderson-Darling (AD) test.
## 4 Results and Analysis
### Error Analysis  
Many sources of error are unavoidable. Examples of these unavoidable sources of error include the calibration coefficients of the CCDs onboard the HJ-IB satellite and drifts in the IRS because of aging of the instruments; mismatches in the timescales of the EC data, which are averaged over time, and the remotely sensed turbulent heat fluxes, which are instantaneous; and the geometric corrections cause a half-pixel bias equal to or less than the deviation of the artificially subjective interpretation. LE is calculated as a residual item in the energy balance equation, whereas the errors of other three energy balance components accumulate in the LE term.