esa-sceva/satcom-chunk-collection huggingface dataset:
# Odor Impact Assessment via Dispersion Model: Comparison of Different Input Meteorological Datasets
## 3 Results and Discussion
### Statistical Metrics  
NMB, as defined in Section 2.5, is computed for each combination of input meteorological data as a function of source distance. This is achieved by averaging the concentration values estimated on discrete receptors located at the same distance from the emission source (Figure 7).  
Figure 7 confirms the optimal agreement between the \"OBS\" and \"HYBRID\" modes, with NMB values very close to 0. As can be noticed, particularly from the results obtained for the Italian domain, \"NO-OBS\" and \"OBS\" show the highest discrepancies. Furthermore, as discussed in the previous results, in the case of area sources, prognostic data appear to underestimate predicted concentrations compared to the \"OBS\" mode. On the other hand, for point sources, the \"NO-OBS\" mode seems to overestimate CALPUFF results compared to the \"HYBRID\" and \"OBS\" modes.  
In the vicinity of the emission, the NMB indicator exhibits low values, with an increasing absolute value trend moving away from the source. This behavior suggests that similar results are achieved in close proximity to the emission source, regardless of the input meteorological settings. A possible explanation lies in the fact that the puffs, near the source, have not yet had time to move by advection, thus not experiencing the different meteorological fields. Conversely, at larger distances, meteorological data have a greater impact on CALPUFF outcomes.  
The FAC2 statistical indicator, reported in Figure 8, reveals a very good agreement between the \"OBS\" and \"HYBRID\" modes, which decreases when introducing prognostic data. FAC2 index values for point sources are generally closer to the optimal value (i.e.,100%, indicating the best fit between the two models) compared to area sources, as previously justified. In the case of point sources, FAC2 statistics are always higher than 70% (except for \"NO-OBS\"-\"OBS\", Cuba, with a percentage of 68%); for area sources, an FAC2 index of less than 60%, in a few cases, indicates a non-optimal agreement.  
Figure 8: FAC2 index (%) for the different input meteorological settings. Point source (**upper**); area source (**down**).  
Figure 7: Normalized Mean Bias (NMB) as a function of source distance computed for the Italian (**left**) and Cuban (**right**) domain.  
Finally, from the comparison of the two simulation domains, it appears that estimated FAC2 index values are always lower in the Cuban domain. This is most likely due to the higher percentage of invalid meteorological data compared to the Italian station.
# Near-Field Aeroacoustics of Spanwise Forcing on a Transonic Wing: A DNS Study
## 2 Numerical Method and Description of the Dataset
### Probing the Pressure Field  
The acoustic information studied in this work is derived from time histories of pressure \(p\) measured for a set of 40 observers (also named 'probes') placed on a circumference of radius \(5c\), centered mid-chord and lying in the \(x\)-\(y\) centerplane. In particular, the analysis focuses on the two uncontrolled cases REF and RREF, and on the two corresponding controlled cases C10 and R10. Additionally, case C12 is also considered. These five simulations can be considered representative of the possible outcomes of applying StTWs on the transonic wing, as cases C10 and R10 feature skin-friction reduction (and lift increase) while case C12 features skin-friction increase (and lift disruption). The pressure signals presented in the remainder of the paper are complete time histories of point probes. Samples are stored every computational time step, i.e., every \(\Delta t=1.5\times 10^{-4}c/U_{\infty}\), and cover a total time span of \(120c/U_{\infty}\). The positions of the probes are depicted in Figure 2. Hereinafter, the angle of the observer is denoted as \(\theta\), and is computed as the angle between the position  
Figure 1: Control parameters of the simulations at constant \(A=0.684U_{\infty}\). The white symbols highlight two cases discussed in the present work: the diamond denotes case C10 (large skin-friction reduction), and the dot denotes case C12 (skin-friction increase). The underlying colormap is the skin-friction reduction rate \(\Delta C_{f,\%}\) obtained from the dataset by [10], i.e., incompressible turbulent channel flow.  
vector of the probe with respect to the center of the profile, and the horizontal positive \(x\)-axis (see Figure 2).  
The overall sound pressure level (OASPL), a common indicator for the intensity of the acoustic signals, is computed in decibels for each observer as  
\[OASPL=20\log\left(\frac{p^{\prime}_{rms}}{p_{ht}}\right) \tag{3}\]  
where \(p^{\prime}_{rms}\) denotes the root-mean-square of the unsteady part of the pressure signals and \(p_{ht}\) is the hearing threshold, conventionally set to \(p_{ht}\) = 20 \(\upmu\)Pa. Since the simulations solve the Navier-Stokes equations in non-dimensional form, the free-stream pressure for retrieving the dimensional value of \(p^{\prime}_{rms}\), used in (3) is set equal to the atmospheric pressure at sea level, i.e., \(p_{atm}\) = 101,325 Pa.
# LoRa Technology Propagation Models for IoT Network Planning in the Amazon Regions
## 2 LoRa Overview
Versatile data rates and payloads: LoRa provides flexibility to accommodate diverse application requirements through adaptive data rates and varying payload sizes. 3. Unlicensed spectrum usage: Operating in unlicensed ISM bands (e.g., 868 MHz, 915 MHz) enables LoRa to bypass the complexities and costs associated with licensed spectrum, facilitating widespread adoption. 4. Low-cost infrastructure: LoRa networks, such as LoRaWAN, require minimal infrastructure investment, making it cost-effective for both research projects and large-scale deployments. Figure 2: The network layers in LoRa technology [25]. Security measures: Robust security features, including encryption and authentication, make LoRa suitable for secure data transmission in applications like healthcare and critical infrastructure monitoring. * Diverse applications: LoRa technology finds applications in various sectors, such as agriculture, environmental monitoring, smart cities, and asset tracking, contributing to data-driven scientific research and improving operational efficiency. Table 1 presents some related works on LoRa applications over water and in environments with vegetation. It provides information on the operating frequency of LoRa technology, the type of condition (line-of-sight (LOS) and/or non-line-of-sight (NLOS)), the study scenario, coverage, and the spreading factor under investigation.
# Optimization of RIS-Assisted 6G NTN Architectures for High-Mobility UAV Communication Scenarios
## 4 Joint Optimization Framework for RIS Configuration and UAV Mobility
### Predictive Coordination Between NTN and RIS
#### 4.2.2 Doppler Effect Modeling  
To capture the impact of high user velocity in UAV-assisted 6G non-terrestrial networks, Doppler shifts are incorporated into the channel model. The instantaneous complex baseband channel coefficients for each link, whether direct or RIS reflected, are expressed as time-varying functions.  
Specifically, the direct link is modeled as  
\[h_{d}(t)=\text{PL}_{d}\cdot 10^{\text{Sh}_{d}/20}\cdot\tilde{h}_{d}\cdot e^{ (2\pi f_{D},t+\phi_{d})}, \tag{14}\]  
where \(\text{PL}_{d}\) is the path loss, \(\text{Sh}_{d}\) represents the shadowing component, \(\tilde{h}_{d}\sim\mathcal{CN}(0,1)\) denotes the small-scale fading, \(f_{D,d}\) is the Doppler shift, and \(\phi_{d}\) is the initial phase of the direct path.  
For the reflected path via the RIS, each element contributes two separate Doppler-affected components: one from the NTN node to the RIS and one from the RIS to the user. The corresponding models are given by  
\[h_{m}(t) =\mathrm{PL}_{h}\cdot 10^{\mathrm{S}h_{h}/20}\cdot\tilde{h}_{m} \cdot e^{i(2\pi f_{D,h_{m}}t+\phi_{h_{m}})}, \tag{15}\] \[g_{m}(t) =\mathrm{PL}_{g}\cdot 10^{\mathrm{S}h_{g}/20}\cdot\tilde{g}_{m} \cdot e^{i(2\pi f_{D,g_{m}}t+\phi_{h_{m}})}, \tag{16}\]  
where \(\tilde{h}_{m},\tilde{g}_{m}\sim\mathcal{CN}(0,1)\) represent the small-scale fading coefficients; \(f_{D,h_{m}}\) and \(f_{D,g_{m}}\) are the Doppler shifts for the NTN-RIS and RIS-user links, respectively; and \(\phi_{h_{m}},\phi_{g_{m}}\) are the corresponding initial phases.  
These Doppler-induced components introduce time-varying phase rotations into the channel response. If left uncorrected, such rotations can cause misalignment of signal paths, resulting in destructive signal combining and an increased probability of outage.
# An Efficient Steganographic Protocol for WebP Files
## 2 Materials and Methods
### Format-Based Methods
#### 2.3.3. Metadata  
Hiding data in WebP metadata may be realized with Exif [69] or XMP [70]. They are both defined for storing metadata created by cameras, smartphones, scanners, and other devices designed for recording images. Exif has tags for Maker, Model, Aperture, and many other settings. XMP is an extendable format that offers XML-like or array-like structures, called Bag, Seq, or Struct.  
According to the specification, metadata may be present in the extended version of WebP images (those with the VP8X chunk). If so, there should be at most one chunk of each type. If there are more such chunks, readers may ignore all except the first one. This opens the possibility for two approaches to data hiding. We may add more than one chunk with the embedded secret in the WebP file, as it will probably be ignored by software. Alternatively, we may conceal a secret in the existing metadata chunk (or insert a new one with our message). In both cases, it is advisable to encrypt concealed information, as metadata may be recovered and seen by anyone, even accidentally while checking camera parameters, etc.  
In Exif metadata, there are multiple tags suitable for data hiding. The list of standard Exif tags is available at [[https://exiv2.org/tags.html](https://exiv2.org/tags.html)]([https://exiv2.org/tags.html](https://exiv2.org/tags.html)) (accessed on 10 August 2023). Depending on the secret format, we may need Ascii type for Latin text, Long for numbers, or Undefined for binary data. Moreover, the standard defines a MakerNote tag, destined for camera manufacturers, in order to place any custom metadata in the file. Usually they describe camera settings, for example, serial number, focusing more, etc. The Exif standard  
Figure 2: Framework of presented methods.  
does not define the structure of the MakerNote. Some manufacturers encrypt portions of the information, which means that such areas may be used for steganographic purposes and even combined with cryptography.  
XMP, on the other hand, is an extensible format. Besides overwriting existing metadata, we may create our own structures and place secret data inside. The available types are Bag, Seq, or Struct, described as follows [68]:  
An \"XmpBag\" is a set of key/value pairs which are represented by XML attributes. An \"XmpSeq\" is an array of metadata similar to a JavaScript or Python array. It is represented by an XML list and can be accessed by index. An XmpStruct is a set of keys to trees of metadata rather like a JavaScript or Python object.
# A Survey of Machine Learning Methods for Time Series Prediction
## 5 Experimental Results and Discussion
### Results
#### 5.3.7 Analysis of Error Metrics in Model Evaluation  
Evaluating the model performance requires the use of appropriate error metrics, which vary depending on whether the task involves classification or regression. This subsection provides a comprehensive analysis of the error metrics employed across the surveyed papers.  
Error Metrics for Classification Models  
The classification models reviewed in this study were evaluated using the following set of metrics:  
* False Positive Rate (FPR);
* Kappa Coefficient (KC);
* Positive Predictive Value (PPV);
* Negative Predictive Value (NPV);
* Receiver-Operating Characteristic (ROC) Curve;
* Matthews Correlation Coefficient (MCC);
* Area Under the ROC Curve (AUC);
* Sensitivity;
* Specificity;
* Recall;
* Precision;
* F1 Score;
* Accuracy.  
The frequency of these metrics' usage is illustrated in Figure 16. The metric names are reported exactly as listed in the surveyed papers, even if some represent equivalent measures (e.g., recall and sensitivity).  
Among the most employed metrics, Recall was used in 35.29% of the studies, Precision in 41.18%, the F1 Score in 47.06%, and Accuracy in 76.47% of the papers.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **TBML Training Advantage (\%)** \\ \hline Study [4] & 4010.33 \\ Study [20] & 181.81 \\ Study [29] & \(-\)22.55 \\ Study [67] & 1251.81 \\ Study [43] & 142.66 \\ Study [45] & 7196.53 \\ Study [74] & 905,140 \\ Study [51] & 235,559.39 \\ Study [55] & 10,145.98 \\ Study [66] & 100,140 \\ Median & 5603.43 \\ Mean & 126,934.94 \\ \hline \hline \end{tabular}
\end{table}
Table 2: TBML training advantage for each surveyed study that conducts training time analyses.  
Error Metrics for Regression Models  
The regression models reviewed in this study were evaluated using the following set of metrics:  
* Index of Agreement (IA);
* Normalized Mean Absolute Percentage Error (NMAPE);
* Prediction of Change in Direction (POCID);
* Mean Normalized Bias (MNB);
* Normalized Mean Bias Error (NMBE);
* Root Mean Squared Percentage Error (RMSPE);
* Root Squared Logarithmic Error (RMSLE);
* Mean;
* Percent Bias (PBIAS);
* R;
* Mean Absolute Scaled Error (MASE);
* Symmetric Mean Absolute Error (SMAPE);
* Coefficient of Variation of the Root Mean Square Error (CVRMSE);
* Nash-Sutcliffe Efficiency (NSE);
* Domain-Specific Error Metrics;
* Mean Squared Error (MSE);
* Mean Absolute Percentage Error (MAPE);
* R\({}^{2}\);
* Mean Absolute Error (MAE);
* Root Mean Squared Error (RMSE).  
The prevalence of these metrics is depicted in Figure 17. Among the traditional metrics, MSE was used in 22.03% of the papers, MAPE in 37.29%, R\({}^{2}\) in 45.76%, MAE in 55.93%, and RMSE in 67.80%.
# Antibacterial Ceramics for Dental Applications
###### Abstract  
Background: The aim of this review was to evaluate the antibacterial properties of functionalized biocearamics for dental applications. Methods: The electronic databases PubMed, Medline, ProQuest, and Google Scholar were used to search for peer-reviewed scientific publications published between 2020 and 2025 that provide insights to answer research questions related to the role of antibacterial-functionalized biocearamics in combating pathogens in dentistry without triggering immune reactions and inflammation, as well as on their efficacy against various pathogens and whether understanding the antibacterial mechanism can promote the development of glass-ceramic and biocearamic with long-term antibacterial activity. The keywords used to answer the research questions were: bioglass, biocearamic, biocompatible, antibacterial, osseointegration, implant, and bioactive materials. Results: Bacterial infections play a key role in the longevity of medical devices. A crucial problem is drug-resistant bacteria. Antibacterial ceramics have received great attention recently because of their long-term antibacterial activity, good mechanical properties, good biocompatibility, and bioactivity. This review provides a detailed examination of the complex interactions between bacteria, immune cells, and biocearamics from a clinical perspective. The focus of the researchers is on developing new-generation biocearamics with multifunctionality, in particular with antibacterial properties that are independent of conventional antibiotics. The highlight of this review is the exploration of biocearamics with dual functions such as antibacterial and bioactive properties, promoting bone regeneration and antibacterial activity, which have the potential to revolutionize implant technology. Another research focus is modifying the implant surface from hydrophilic to hydrophobic in order to increase the antibacterial activity of biocearamics. Conclusions: The aim of this review is to help researchers understand the current state-of-the-art antibacterial activities of biocearamics, which could promote the development of antibacterial ceramics and their clinical application.  
bioglass; biocearamics; biocompatible; antibacterial; osseointegration; implant; bioactive materials +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
This was the first study to investigate the effect of MeJA and SA jointly on quality losses, antioxidant properties and enzyme activities of apricot fruit in CS and SL conditions.  
Our results show that treatments of both SA and MeJA decreased the CI and FD of apricot fruit in CS and SL periods. Both elicitors ensured high total antioxidant capacity, total polyphenic and carotenoids contents, and enhanced the enzyme activity of PAL, POD, SOD and CAT in CS and SL conditions; as a result, the storability of apricot fruit is increased. Among the 45 pair-variables, Pearson's correlation coefficients of 23 and 9 pair-variables correlated significantly in the treatments of MeJA and SA, respectively. Among these pair-variables, six pair-variables (FD vs. CI, PAL vs. CAT,PAL vs. SOD, TAC vs. SOD, TAC vs. FD, and AAC vs. CI) were significant in both the MeJA and the SA, indicating connections among fruit quality loss, antioxidant parameters and enzyme activities. The relationship between the six pair-variables was further confirmed by linear regression analysis with \(r=0.716\)-\(0.932\), \(p=0.03\)-\(0.001\) and with \(r=0.703\)-\(0.889\), \(p=0.03\)-\(0.001\) for the MeJA and SA treatments. PCA explained 89.3% of the total variance and PC1 accounted for 55.6% of the variance and correlated with the CI, FD, TAC, TSPC, TCC, PAL, and SOD variables. Variables of PC1 were efficient in discriminating the treatments. The treatments of MeJA and SA are practically useful and inexpensive techniques to maintain several quality attributes of apricot fruit during both CS and SL conditions.  
Conceptualization, A.E., I.J.H., A.A., Z.S. and J.N.; methodology, A.E.; A.H. and A.A.; software, S.S.; validation, A.E., I.J.H., A.H. and S.S.; formal analysis, I.J.H. and A.E.; investigation, A.E.; resources, I.J.H.; data curation, A.E.; writing--original draft preparation, A.E. and I.J.H.; writing--review and editing, S.S., J.N., Z.S. and B.M.; visualization, A.E. and I.J.H.; supervision, I.J.H.; project administration, B.M.; funding acquisition, I.J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Hungarian Scientific Research Funds (K 131478), by a [PERSON] Bolyai Research Fellowship awarded to [PERSON], by the European Union and the State of Hungary, co-financed by the European Social Fund in the framework of TAMOP-4.2.4.A/2-11/1-2012-0001 'National Excellence Program' under project number A2-SZJ-TOK-13-0061, and by The research was financed by the Thematic Excellence Programme of the Ministry for Innovation and Technology in Hungary, within the framework of the Space Sciences thematic programme of the University of Debrecen, and by the Thematic Excellence Programme of the Ministry for Innovation and Technology in Hungary (ED_18-1-2019-0028), within the framework of the climate change thematic programme of the University of Debrecen.  
We also thank to Gyumolcsert Ltd.  
The authors declare no conflict of interest.
## 2 Methods
### Error Similarity Analysis with TCP Calibration Uncertainties
Particularly, for each \(\theta_{i}\), \(\theta_{j}\in\mathbb{R}^{6}\) represents the particular joint angle vector for which the Euclidean distance from \(\theta_{i}\) is \(h\). For each \(\theta_{i}\), the total amount of corresponding \(\theta_{j}\) is denoted as \(N(h_{i})\). The Euclidean distance h between two joint configurations can be expressed by Euclid norm as  
\[h=\big{\|}\theta_{i}-\theta_{j}\big{\|}_{2} \tag{6}\]  
\(\Delta P(\theta_{i}),\ \Delta P(\theta_{j})\in\mathbb{R}^{3}\) represents the TCP positioning errors of the corresponding joint configurations and takes the form of  
\[\Delta P(\theta_{\ell})=P(\theta_{\ell})-(P(\theta_{\ell}+\Delta\theta))\ \ell=i,\ j \tag{7}\]  
where \(P(\theta_{\ell})\) represents the theoretical position of TCP with the joint configuration of \(\theta_{\ell}\). \(\Delta\theta\) is the deviation of joint angles. However, Equation (7) holds only under the assumption that there is no error in calibrating the TCP frame. When the error exists, Equation (7) must be expanded to the following homogeneous coordinates form. \[ \begin{bmatrix}\Delta\mathbf{P}^{\prime}(\theta_{\ell})\\ 1\end{bmatrix} = \begin{pmatrix}\begin{bmatrix}\mathbf{P}(\theta_{\ell})\\ 1\end{bmatrix}-\begin{bmatrix}\mathbf{P}(\theta_{\ell}+\Delta\theta)\\ 1\end{bmatrix}\cdot\Delta^{\text{W}}\,\mathbf{T}_{\text{T}}\end{pmatrix} \cdot \begin{bmatrix}\mathbf{O}_{3\times 1}\\ 1\end{bmatrix}  \tag{8}\]  
where \(\Delta^{\text{W}}\,\mathbf{T}_{\text{T}}\) is the calibration error of the TCP frame. \(\mathbf{O}_{3\times 1}\) represents the 0-column vector in 3 dimensions. Considering Equation (2), because the only variable is \(\theta_{i}\). \(\text{Trans}(0,0,d_{i})\), \(\text{Trans}(a_{i},0,0)\) and \(\text{Rot}(0,0,\alpha_{i})\) in Equation (2) are all constant matrices. In order to simplify the operation, it can be assumed that  
\[ \begin{array}{c}d_{i}=a_{i}=\alpha_{i}=0,\ i=1,\cdots,6\\ \Delta\theta=\left[\Delta\theta_{1},\cdots,\Delta\theta_{6}\right]\end{array}  \tag{9}\]  
which leads to \(\text{Trans}(0,0,d_{i})\), \(\text{Trans}(a_{i},0,0)\) and \(\text{Rot}(0,0,\alpha_{i})\) transforming into the identity matrix \(\mathbf{I}_{4\times 4}\). In addition, since the TCP frame calibration error \(\Delta^{\text{W}}\,\mathbf{T}_{\text{T}}\) is independent of \({}^{\text{W}}\,\mathbf{T}_{\text{T}}\), it can be assumed that  
\[\begin{array}{c}{}^{\text{W}}\,\mathbf{T}_{\text{T}}=