esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Solution for Estimating Pedestrian Locations from UAV Imagery
## 4 Materials and Methods
### Pedestrian Localization from Video
Values \(C_{hor}\) and \(C_{ver}\) are the horizontal and vertical angles of the camera. Given the knowledge of the altitude difference between the ground and UAV, it is possible to calculate the flat distance between the UAV and a detected person. Flat distance is the distance between objects without taking altitude into account. We use the following equation to calculate the distance:  
\[flatDist=2\times|alt\times tan(rad(90-G_{ver}))| \tag{6}\]  
where \(flatDist\) is the flat distance between the camera and an object, \(alt\) is the height difference, \(rad()\) represents the conversion between degrees and radians, and, finally, \(G_{ver}\) is the previously acquired global vertical angle. The resulting distance is represented in meters by default. To convert the value into geographic coordinates, a conversion equation is as follows:  
\[geoDist=\frac{flatDist}{kpd\times 1000} \tag{7}\]  
since a geographic degree represents an approximate distance of \(kpd=111.32\) km in metric units, we multiply this value by 1000 to convert it into meters. The result of the following division is \(geoDist\), which is the geographic distance in degrees. Given the knowledge of geographic distance \(geoDist\) and the global horizontal angle \(G_{hor}\), it is possible to calculate the geographic directional vector \(P\). This vector specifies the exact location of a person relative to the UAV. We calculate this using the following equations:  
\[P=(Px,Py) \tag{8}\]  
\[vFOV_{rad}=atan(\frac{x}{y}\times tan(\frac{hFOV_{rad}}{2})) \tag{1}\]0  
\[vFOV_{rad}=atan(\frac{x}{y}\times tan(\frac{hFOV_{rad}}{2})) \tag{1}\]1 Horizontal angles. \[vFOV_{rad}=atan(\frac{x}{y}\times tan(\frac{hFOV_{rad}}{2})) \tag{1}\]2  
where \(Px\) is the geographic distance in latitude and \(Py\) is an analogous value for longitude. If we are given the geographic location of the UAV, which we may represent as \(D=(Dx,Dy)\), then it is finally possible to calculate the real-world geographic coordinates of a detected person. By performing the addition between the \(P\) and \(D\) vectors, we acquire the latitude and longitude of the person as \(LatLon=(Lat,Lon)\). These values are saved into an output data file, together with the video frame timestamp for the subsequent cluster analysis.
## 3 System Model and Architecture Design
### System Model Based on SBD2D Data Integration  
Before sending the data to the base station, this method combines and aggregates the data from several different nodes. By utilizing this method, redundant transmissions are cut down, which, in turn, helps to save energy. It removes the requirement for centralized routing, which removes an additional potential energy drain, see Figure 5. This strategy cuts down on the energy usage that is normally involved with transmitting data over several intermediate nodes. Buying out this model, we will benefit from the following:  
* Energy Efficiency: smart beekeeping systems can significantly increase their energy efficiency with the incorporation of D2D communication and data integration techniques. As opposed to conventional centralized communication techniques, the energy necessary for transferring data through intermediaries is drastically reduced when communicating directly between nodes. Data integration methods also reduce unnecessary transfers, which helps to save power.
* Reduced Transmission Overhead: fewer transmissions are needed to send information to the base station when the data are integrated at the node level. Instead of delivering numerous separate data packets, an aggregated data packet is sent. In addition to saving energy, network optimization is also achieved.
* Minimized Latency: D2D communication and data integration enables real-time data exchange between neighboring nodes; in this instance, nodes from one beehive to another, ensuring a low transmission latency. This immediate data transmission enables quick decisions and responses to changes in hive conditions, thereby enhancing the hive management and bee productivity
## 3 Results and Discussion
### Fourier-Transform Infrared Spectroscopy (FTIR)  
Our results showed ITZ peaks at 1180 cm\({}^{-1}\) (C-N stretching) and 1138 cm\({}^{-1}\) (C-O-C ring cyclic ether stretching). Peaks attributable to alkyl C-H stretch vibrations were detected at 2800-3100 cm\({}^{-1}\)[29]. The FTIR spectrum of fiber-forming PVP showed a broad band at 3500-3200 cm\({}^{-1}\) assigned to O-H stretching of the hydroxyl groups and at 1647 cm\({}^{-1}\) for C=O stretching. The peak at 1437 cm\({}^{-1}\) was assigned to the C-H deformation of the CH\({}_{2}\) group [6; 35].  
The fibrous sheets showed peaks at 1179 cm\({}^{-1}\) and 1123 cm\({}^{-1}\), as well as a broad band at 3500-3200 cm\({}^{-1}\) assigned to the O-H stretching of the hydroxyl groups (from PVP).  
The peaks were very similar to those of pure ITZ, presenting a shift to lower wave numbers and weaker peak intensities. Shifts in FTIR spectrum peaks suggested, in the case of drug-polymer interactions, hydrogen bonding [34].
# Review of V2X-IoT Standards and Frameworks for ITS Applications
###### Abstract  
The intelligent transport system (ITS) has become one of the most globally researched topics with a lot of investment and development resources being dedicated into it due to its foreseen impact on the economic growth of the transport sector. Currently there are two main vehicle-to-everything (V2X) technologies, whose primary application is focused on ITS, backed up by the key players of various automotive, telecommunication and transport industries: dedicated short-range communications (DSRC) and cellular vehicle-to-everything (C-V2X), respectively based on IEEE 802.11p and 3 GPP LTE/5G NR. While DSRC already has deployments, C-V2X is expected to see larger scale trails and deployments in 2020. In this work, the authors provide insight and review into two main V2X technologies, DSRC and C-V2X, their core parameters, shortcomings and limitations, and explore the need for integration of IoT-based technologies into modern ITS solutions. A comprehensive overview and analysis of currently commercially available V2X products, their sub-blocks and features is provided.  
V2X; DSRC; C-V2X; 5G NR; ITS; IoT 2020
## 5 Conclusions  
Models of selected machine learning algorithms were trained to classify rock bursts and non-destructive tremors in a selected hard coal mine. (1) The dominance of strong and non-destructive tremors over rock bursts in the database was considered. (2) The database corresponding to real conditions required balancing, therefore the ADASYN algorithm was used. (3) The GEO index was used as one of the features, describing the proneness of the entire seam-surrounding rock system to rock burst. (4) Rock bursts were best classified by an ensemble classifier composed of MLP, AdaBoost, GB, KNN, and GNB classifiers. On average, almost three-quarters of rock bursts in the test datasets were correctly classified by the ensemble classifier. (5) Developing a machine learning-based rock burst classification could help prevent accidents by alerting miners and engineers in advance of potential rock bursts. However, further research in this area should be continued using various machine learning algorithms and their configurations in the ensemble classifier.  
Conceptualization, L.W. and M.B.; Methodology, L.W.; Software, S.I.; Validation, L.W., S.I. and D.B.A.; Formal analysis, L.W. and S.I.; Investigation, L.W. and S.I.; Resources, L.W. and M.B.; Data curation, L.W. and M.B.; Writing--original draft preparation, L.W., M.B., S.I. and D.B.A.; Writing--review and editing, L.W. and D.B.A.; Visualization, L.W.; Supervision, D.B.A.; Project administration, L.W.; Funding acquisition, L.W. All authors have read and agreed to the published version of the manuscript. This research was funded by the Ministry of Science and Higher Education, Republic of Poland (Statutory Activity of the Central Mining Institute, Grant No. 11133011).  
Not applicable.  
Restrictions apply to the availability of these data. Data were obtained from the Polish Mining Group and are available from the authors with the permission of the Polish Mining Group.  
The authors would like to thank the Polish Mining Group for providing the data used for the calculations and discussing the results.  
The authors declare no conflicts of interest.
# Effects of Blade Extension on Power Production and Ultimate Loads of Wind Turbines
## 5 Discussion on the Economic Effect  
The economic effect of technological transformation is of high concern. In this section, we will analyze the cost and revenue changes with blade extension.  
The cost of the materials, the mold and the blade tip manufacture is approximately evaluated according to engineering experience, as shown in Table 18. Assuming that the annual average wind speed is 8.5 m/s, the annual energy production of wind turbines with different blade lengths is listed in Table 4. The unit price of electricity is set at 0.45 yuan/kWh. According to the cost and revenue in Table 18, the payback period is about 4.25 years, 3.32 years and 2.83 years for the extension lengths of 1 m, 1.5 m and 2 m, respectively. As can be observed, both the costs and revenue increase with the extension length. However, the payback period is shortened with blade lengthening, which indicates that the economy improves with the increase in blade length within the safety margin.
## Materials and Methods
### Formulation of the GRAMI Model  
The four processes (Figure 2) involved in simulating daily rice growth are: (1) interception and absorption of the incident solar radiation by the leaves; (2) calculation of growing degree-days (GDD); (3) production of the new dry mass by the leaf canopy; and (4) determination of the LAI partitioning of the new dry mass. The details of these procedures and related equations are described in Appendix A.  
In this study, we applied the same initial conditions and parameter values determined in the earlier studies (Table A1) [18; 21; 23].  
LAI is a three-dimensional concept, while the reflectance of plants to solar radiation is a two-dimensional concept because it is electronically recorded as a two-dimensional data for the canopies of crops or mostly the top surface of the plants. We presumed that a log-log regression model, with a slope approximately 2/3, could describe the relationship between reflectance and LAI. Based on this theory, the correlations between five VIs (MTVI, NDVI, RDVI, and OSAVI) and LAI were formulated using log-log linear regression models. For each VI, labelled l = 1, 2, 3, 4, and 5, respectively, an empirical model was framed as follows:  
\[\log(\mathrm{VI_{t}})=\alpha_{VI}+\beta_{VI}\log(LAI_{t})+\epsilon_{t} \tag{6}\]  
where \(\alpha_{VI},\ \beta_{VI}\), and \(\epsilon_{t}\) (\(\sim N\Big{(}0,\sigma_{VI}^{2}\Big{)}\) represent intercept, slope, and error of the linear regression model, respectively.  
The evolution of the LAI for each pixel was explained by the GRAMI-rice model, using four parameters \(\theta=(L_{0},\,a,\,b,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,  
Step 3: Generate the simulated curve for each pixel from the estimated \(\psi\) in Step 2.  
Step 4: Update \(\mu\), D as the sample means and sample variances of the estimates in Step 2.  
In this procedure, the parameter \(\psi\) was estimated by minimizing the above function, and the optimization was performed using the POWELL optimization routine [51] for one-point simulation cases and the Quasi-Newton minimizer [52] for two-dimentional simulation cases.
## 4 Method Evolution and Future Improvements
### Method Development Milestones
Some possible pathways for improvement are presented below, but their effect will need to be carefully examined, in isolation, in future studies.
## 3 System Model  
The two-tier macro-femto heterogeneous network is where a macrocell and femtocell work as an essential primary system and secondary system, respectively, both systems share the same frequency (f = 2 GHz) spectrum [30]. \(P_{M}\) represents the downlink transmission power of the eNB. Similarly, the HeNBs transmit with a fixed power \(P_{F}\). In the LTE-Advanced (LTE-A) network, the system resources are divided along time slots and frequency sub-carriers. These resources are scheduled or distributed to users in units of Physical Resource Blocks (PRBs). We assume that the \(K\) PRBs are distributed uniformly among the MUEs in the macro-cell and the same resources are reused within each femtocell. In this paper, we consider an inadequately deployed HeNB in an indoor environment. The HeNB is linked to a macrocell through a broadband Internet backhaul link. The femtocell works in close access mode, where only CSG users can subscribe the services of the femtocell. The access styles of HeNB plays an important role in dealing with the interference. The interference is much sever in the closed access mode. Therefore, we consider the CSG mode to take the worst interference scenario into account.  
Figure 1 shows the interference scenario of inter-cell interference (ICI) between the elements of the primary and secondary systems. In this case, the downlink link transmission of HeNB is the source of interference to MUE existing in its close proximity. We adopted the 3 GPP LTE-A pathloss model for urban deployment of the femtocell [31].
# Induction Coil Design Considerations for High-Frequency Domestic Cooktops
## 3 General Structure of Induction Coils  
Induction coils are critical to ensuring optimum and efficient power transfer in domestic induction heating applications. The coil-pan equivalent circuit will determine the power transferred to the load and the switching frequency. The basic components of the induction coils are windings, ferrites, and an aluminum plate. The structure of an induction coil is shown in Figure 6. Windings and ferrites are used to create the required magnetic field. The aluminum plate acts as a cooler for the ferrites and prevents the electronic circuit from being affected by the magnetic field. The chemical and physical properties of the aluminum plates used in commercialized induction cookers are similar for all products.  
The number of windings and magnetic cores are the main parameters that determine the equivalent inductance and resistance. The value of the equivalent inductance determines the switching frequency with the resonance capacitor, while the equivalent resistance determines the power ratings. The inductance value increases with the number of turns. However, the higher number of turns means higher copper loss due to the skin effect that occurs in the windings, lower efficiency, and lower performance. In addition, additional losses lead to increased operating temperatures that could lead to magnetic core saturation. The number of magnetic cores increases the equivalent inductance and resistance values. Meanwhile, magnetic cores are so critical that they directly affect magnetic saturation and magnetic cores suitable for high frequencies should be preferred. As a result of the FEM  
Figure 5: The actual LCR meter measurements with cast-iron pan on a 210 mm induction coil.  
analysis, an appropriate saturation value should be selected according to the maximum magnetic field to which the magnetic core will be exposed. When an induction coil saturates, it causes catastrophic results, such as short-circuits in semiconductor switches. The higher the number of magnetic cores, the lower the risk of magnetic saturation. Figure 7 summarizes basic induction coil design parameters and their effects.  
As the number of windings and cores in the induction coil increases, the equivalent inductance increases, and since the induced magnetic flux increases, the equivalent resistance value to be seen will also increase. While decreasing the number of cores increases the risk of magnetic saturation, increasing it will increase the equivalent inductance and resistance values.
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 4 In-Band Sensor Network Requirements
### Control Points  
A second critical factor in \(\overline{\text{DVO}}\) deployment is the establishment of control points along the line. These control points must be able to control voltage based on input from the sensor network. Control points can be deployed at the substation controlling the LTC (Load Tap Changer) as well as at voltage regulators, capacitor banks, or other control points anywhere along the line. The granularity of \(\overline{\text{DVO}}\) voltage control is directly related to the number of control points deployed in the distribution grid. This is shown conceptually in Figure 1. In the figure, the area under the curve decreases, and hence the \(\overline{\text{DVO}}\) savings increases, as the number of control points deployed in the network is optimized.  
Figure 1: Control Points Vs. Voltage Level.
## 2 Materials and Methods
### Selection of Retrieval Window  
The SIF retrieval in this study is conducted near the FLs around 757 nm in the far-red band. The satellite spectra in the far-red FLs band exhibit prominent slope characteristicsdue to vegetation canopy reflectance effects. Through SVD of the non-SIF radiance spectra, these characteristics are extracted and concentrated in the third singular vector, Sv3. Despite the significant difference in slope between singular vector Sv3 and the SIF spectrum, Sv3 itself cannot fit the SIF spectrum, but it enhances the fitting capability of singular vectors to the SIF spectrum within the forward model. Inversion results indicate that there is an order of magnitude difference between the coefficients of Sv3 and the Fs state vector, compensating for the significant difference in slope between them. The intensity difference caused by the coefficients can be compensated for by other singular vectors.  
To avoid overfitting singular vectors to SIF spectra, we have improved the traditional retrieval window. The enhanced retrieval window introduces the edge portion of the O\({}_{2}\) absorption line, which occupies less than one-tenth of the entire retrieval window, thereby having minimal impact on Fs affected only by atmospheric absorption in the observation direction. Furthermore, benefiting from SVD's feature extraction of the non-SIF radiance spectra, the introduction of the O\({}_{2}\) absorption band can significantly alter the morphological characteristics of specific singular vectors. As shown in Figure 1, Sv3 contains two distinct slope features, caused by O\({}_{2}\) absorption and surface reflection, respectively. These markedly different slope features effectively restrict the fitting of singular vectors to SIF spectra with a single slope characteristic, thereby achieving better decoupling between Fs and singular vectors.  
In the narrow retrieval window, the complexity of the SIF spectrum is low, making it prone to overfitting. Although using a larger window for SIF retrieval helps in decoupling Fs from singular vectors, it results in the loss of the advantage of high Fs intensity in the far-red band, thus not improving the retrieval results. By introducing the O\({}_{2}\) absorption band, better decoupling of Fs can be achieved while retaining the intensity advantage. However, the introduction of the O\({}_{2}\) absorption band complicates the SIF inversion, requiring a reconsideration of the construction of the forward model and the selection of singular vectors.
## 5 Discussion and Conclusions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(z\)/\(L\)} & \multirow{2}{*}{\(C_{n}^{2}\) [m\({}^{-2/3}\)]} & \multirow{2}{*}{\(\tau_{\text{cor}}\) [ms]} & \multirow{2}{*}{\(\sigma_{J}^{2}\)} & \(z\)/\(L\) **Estimate** & \(C_{n}^{2}\) **Estimate** & \(z\)/\(L\) **Estimate** & \(C_{n}^{2}\) **Estimate after** \\  & & & **after 1 st** & **after 1 st** & **after 2 nd** & **2 nd Iteration** \\  & & & **Iteration** & **Iteration** & **Iteration** & **[m\({}^{-2/3}\)]** \\ \hline
0.1 & \(1\times 10^{-14}\) & 0.73 & 0.268 & \([0.10;0.15]\) & \([0.6;1.0]\times 10^{-14}\) & \([0.10;0.12]\) & \([0.8;1.0]\times 10^{-14}\) \\
0.5 & \(1\times 10^{-15}\) & 0.21 & 0.097 & \([0.36;0.51]\) & \([0.9;1.0]\times 10^{-15}\) & \([0.49;0.51]\) & \([0.9;1.0]\times 10^{-15}\) \\
0.9 & \(1\times 10^{-16}\) & 0.08 & 0.004 & \([0.83;0.90]\) & \([0.7;1.0]\times 10^{-16}\) & \([0.88;0.90]\) & \([0.8;1.0]\times 10^{-16}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Examples of turbulence layer characterization  
**Author Contributions:** Conceptualization, V.A.K., M.A.V., and V.S.R.G. ; Methodology, V.A.K. and S.L.L. ; Software, V.A.K. ; Validation, V.A.K. ; Formal analysis, V.A.K., S.L.L., and M.A.V. ; Investigation, V.A.K., S.L.L., M.A.V., and V.S.R.G. ; Writing--original draft preparation, V.A.K. and S.L.L. ; Writing--review and editing, V.A.K., S.L.L., M.A.V., and V.S.R.G. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was supported by the U.S. Air Force Office of Scientific Research (AFOSR) MURI Contract #FA9550-12-1-0449 and Joint Directed Energy Transition Office (DE-JTO) through ONR contract N00014-17-1-2535. **Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Results
### Microhardness
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Statistical** **Parameters**} & \multicolumn{5}{c}{**Hardness (HV\({}_{2}\))**} \\ \cline{2-6}  & **B4C** & **B4C 0.5\%** & **B4C 1\%** & **B4C 2\%** & **B4C 3\%** \\  & **monolithic** & **HEA** & **HEA** & **HEA** & **HEA** \\ \hline Mean & 2966.67 & 3079.52 & 3016.71 & 3268.00 & 3308.57 \\ Median & 2990.00 & 3073.00 & 3011.00 & 3288.00 & 3319.00 \\ Standard deviation & 181.09 & 296.82 & 211.32 & 225.16 & 258.80 \\ Minimum & 2621.00 & 2432.00 & 2568.00 & 2799.00 & 2737.00 \\ Maximum & 3381.00 & 3862.00 & 3550.00 & 3859.00 & 3813.00 \\ Quartile 25 & 2812.00 & 2876.00 & 2913.00 & 3112.00 & 3182.00 \\ Quartile 50 & 2990.00 & 3073.00 & 3011.00 & 3288.00 & 3319.00 \\ Quartile 75 & 3074.00 & 3288.75 & 3100.00 & 3405.00 & 3514.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical parameters of Vickers hardness measurements for B4C composites and monolithic B4C sample. Figure 9: Histogram of Vickers hardness for (**a**) monolithic B4C; (**b**) B4C.05; (**c**) B4C.1; (**d**) B4C.2; and (**e**) B4C.3. Histograms charts reveal how the addition of HEA affects the hardness distribution. They show the hardness values of all indentations classified into different ranges. Composites with high HEA doping show higher hardness measures and smaller spread, suggesting a more homogenous hardness profile. The 0.5% HEA composite shows lower hardness measures and a wider distribution, which indicates greater variability. In addition, box plots and normal distribution graphs of the Vickers hardness values obtained (see Figure 10) were generated in order to complete the statistical analysis. Information about the mechanical behavior of all composites and the impact of varying doping concentrations is obtained. The analysis of quartiles and Gaussian curves demonstrates that the 1% and 2% of HEA samples have a more consistent and predictable hardness distribution. The 0.5% HEA concentration composite possesses a wider spread and more variability, indicating continuous microstructural modifications. On the other hand, the B4C.2 sample has less variability, confirming the SEM results. These results prove the idea that HEA doping  
Figure 10: Vickers hardness for B\({}_{4}\)C composites and monolithic sample comparison: (**a**) distribution of Vickers hardness data through its quartiles; (**b**) probability distribution of the hardness values. improves the B\({}_{4}\)C ceramic's mechanical performance, with the greatest results shown for higher HEA concentrations.