esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.2.2 RadCalNet Data Process  
Based on the automatic ground measurement data of four calibration sites provided by RadCalNet, the 6S atmospheric radiative transfer model was used to calculate the at-sensor TOA radiance. The BOA reflectance and atmosphere parameters (i.e., aerosol optical depth, water vapor content, and ozone content) were extracted from the RadCalNet output file based on the satellite overpass time. The RadCalNet ground measurement data, observation geometry parameters, and sensor spectral response function were input into the 6S model to simulate the quantities in the atmospheric radiative transfer equation, which is simplified as the following Equation (4):  
\[\rho_{i}^{*}(\theta_{s},\theta_{v},\varphi_{s},\varphi_{v})=\rho_{0i}(\theta_{ s},\theta_{v},\varphi_{s},\varphi_{v})+\rho_{i}\cdot\tau_{gi}\times\frac{\tau_{ \uparrow i}\cdot\tau_{\downarrow i}}{1-\rho_{i}s_{i}} \tag{4}\]  
where \(\rho_{0i}\) is the upward scattering reflectance generated by the atmosphere, \(\rho_{i}\) is the surface reflectance, \(\theta_{s},\theta_{v},\varphi_{s},\varphi_{v}\) are the solar zenith angle, sensor zenith angle, solar azimuth angle, and sensor azimuth angle, respectively, \(\tau_{gi}\) is the total absorption transmittance of the atmosphere taken into account in both incident and observation directions, \(\tau_{\uparrow i}\) is the scattering transmittance in the observation direction, \(\tau_{\downarrow i}\) is the scattering transmittance in the incident direction of the solar, and \(s_{i}\) is the hemispherical reflectance of the atmosphere.  
Then, the at-sensor radiance (i.e., TOA radiance) \(L_{i}^{*}\) is calculated according to the following Equation (5):  
\[L_{i}^{*}=\frac{\rho_{i}^{*}\cos(\theta_{s})E_{i}}{\pi\cdot d_{s}} \tag{5}\]  
where \(d_{s}\) is the correction factor for the distance between the Sun and the Earth, \(\rho_{i}^{*}\) is the TOA reflectance, and \(E_{i}\) is the solar irradiance.
## 3 Results
### Results of the Medial Longitudinal Arch (MLA) Height Measurement  
The results of the MLA height measurements are shown in Table 2. The measurements indicated that the exercise group exhibited an increased MLA height under the non-weight-bearing and weight-bearing conditions. However, these differences were not statistically significant. The group engaging in toe exercises exhibited an increased height in the MLA under both non-weight-bearing and weight-bearing conditions, although these differences were not statistically significant.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable** & **Group** & **Pre** & **Post** & **Group and Time** & **F** & \(p\) \\ \hline \multirow{3}{*}{NWL} & EX (n = 20) & 54.80 \(\pm\) 4.11 & 57.05 \(\pm\) 3.36 & G: T: & 0.037 & 0.848 \\  & CON (n = 20) & 55.05 \(\pm\) 4.67 & 55.45 \(\pm\) 4.07 & G \(\times\) T & 2.438 & 0.123 \\ \hline \multirow{3}{*}{NWR} & EX (n = 20) & 54.95 \(\pm\) 3.81 & 57.30.3.31 & G: T: & 0.063 & 0.803 \\  & CON (n = 20) & 55.90 \(\pm\) 4.59 & 55.91 \(\pm\) 4.21 & G \(\times\) T & 1.713 & 0.194 \\ \hline \multirow{3}{*}{WL} & EX (n = 20) & 49.90 \(\pm\) 3.16 & 51.40 \(\pm\) 3.56 & G: T: & 0.600 & 0.441 \\  & CON (n = 20) & 49.20 \(\pm\) 4.89 & 49.15 \(\pm\) 4.84 & G \(\times\) T & 0.685 & 0.410 \\ \hline \multirow{3}{*}{WR} & EX (n = 20) & 49.85 \(\pm\) 2.94 & 51.40 \(\pm\) 3.80 & G: & 1.990 & 0.162 \\  & CON (n = 20) & 49.15 \(\pm\) 4.88 & 49.50 \(\pm\) 4.58 & G \(\times\) T & 1.063 & 0.306 \\ \hline \multirow{3}{*}{WL} & \multicolumn{6}{c}{Values are means \(\pm\) SDs; EX, exercise group; CON, control group; NWL, non-weight load left; NWR, weight load left; WR, weight load left; GR, group; T, time; G \(\times\) T, group and time.} \\ \cline{2-6}  & \multicolumn{6}{c}{} \\ \end{tabular}
\end{table}
Table 2: Medial longitudinal arch (MLA) height: changes in the MLA height (non-weight-bearing and weight-bearing) for the exercise and control groups before and after the intervention, with \(p\)-values for the group and time comparisons.
## 3 Results and Discussion
### Oil Film Experiments  
Figure 14 shows side views of oil film measurements for (a) a smooth ball, (b) Telstar 18, (c) Krasava, (d) Finale, and (e) Vantaggio. The wind direction was set from left to right. All measurements were performed at supercritical conditions with Re = 4.3 \(\times\) 10\({}^{5}\). In the smooth sphere (a), the first and second separation points appeared on the surface. This feature is consistent with a previous study [4]. The dashed line shows the line of separation bubbles that reattach to the sphere's surface after the boundary layer has detached. The solid line shows the point at which the flow around the sphere finally separated. As shown in this figure, detachment is delayed in the turbulent flow, indicating that the drag coefficient under the turbulent flow is small. On the other hand, only the turbulent separation line is observed in the soccer ball with panel grooves. This result indicates that the panel grooves have shifted the flow from laminar to turbulent flow. The separation point is nearly the same for all soccer balls, corresponding to the fact that the \(C_{D}\) values were almost the same for the drag measurements at a velocity of 29.6 m/s.  
Figure 12: The relationship between groove volume and drag coefficient (\(C_{D}\)) in the subcritical regions.  
Figure 13: The relationship between groove volume and drag coefficient (\(C_{D}\)) in the supercritical regions.  
Figure 15 shows the relationship between the \(C_{D}\) values in the supercritical region obtained from the fluid force measurements and the separation position in the oil film experiment. The \(C_{D}\) value increases as the separation angle increases, and this correlation coefficient is as large as 0.90. Therefore, the position of the detachment point changes depending on the shape of the ball groove in the supercritical region, and the drag coefficient changes accordingly.
## 3 Overview of the Proposed Method
### Generate Situations at the Logical Level
#### 4.3.1 Define Parameters and Value Ranges  
Different parameters (variables) have to be considered for the generation of the scenarios and, more specifically, for the description of the scenarios. For examples, we have qualitative variables (nominal, ordinal, binary) such as \"Type of users (pedestrian, cyclist, vehicle)\" or \"Level of brightness (Low, Medium, High, Very high), and quantitative variables (discrete or continuous) such as \"Number of lanes [(1, 2, 3 and more)\" or \"Vehicle_speed (0-30 km/h).  
We define the sensitivity of AV to a range of value as follows: the degree to which the range of value can lure the automation function.  
As a situation is defined by a combination of different parameters, the more the AV is sensitive to the range of value of each parameter, the more it is sensitive to this situation.  
As these parameters are numerous and most of them have continuous value ranges, we thought it appropriate to distinguish value ranges which make a significant contribution in estimating the achievement of the AV safety performance.  
This distinction is made with the help of a safety expert. However, partitioning the value ranges into classes (i.e., exclusive but complementary value ranges) is intended to ensure that each possible situation that the vehicle may encounter and to which it may be sensitive is properly defined. For each parameter, the safety expert is asked to define different classes of variation. For each class, he/she examines the impact of that class on each AV technology. If two close classes have a similar impact, they may be combined into a single class. The number of ranges for each parameter must be determined according to the different sensitivities of the AV technologies, but the safety expert must reduce this number as much as possible.  
Consider the example of the \"rainfall level\" parameter. Its space variation is from 1 to 100 mm/h. We propose to divide it into two classes of variation: Light = 1-7 mm/h and Heavy = 8-100 mm/h. As some sensor technologies are sensitive to heavy rainfall, such a partition makes it possible to highlight the range of heavy rainfall. When combined with other parameters, this range further increases the possibility of logical scenarios that have a high impact on criticality.
# Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning
## 1 Introduction
However, this approach is prone to error propagation from the classification maps, underscoring the need to improve classification accuracy. The second approach directly compares two images from different dates. Deep neural network models, such as Improved UNet++ [49], are specifically designed for this task. These models use multi-temporal data in an end-to-end manner, analyzing spectral, spatial, and structural features to produce a final change map. This method can also be accomplished using machine learning algorithms [50]. Although numerous studies have explored remote sensing data and developed algorithms for forest mapping, few have specifically focused on organ forests, either for general mapping or deforestation detection, largely due to the limited availability of organ tree-related data [51]. Two studies focused on mapping organ trees near Essaouira, close to Agadir, using the NDVI index derived from Sentinel-2 time series data and the Support Vector Machine (SVM) algorithm, achieving overall accuracies of 89.78% [52] and 92.60% [53], respectively. Another study mapped organ using Sentinel-1 time series, Sentinel-2 data, and three machine learning algorithms, in addition to a layer of the missing Shuttle Radar Topography Mission Digital Elevation Model (DEM), achieving the highest accuracy of 93.25% [54]. Building on this foundation, our team researched mapping organ deforestation using deep learning algorithms and a patch-based approach [20; 32; 55]. This study aims to further enhance these results. Recognizing the ecological and economic importance of organ trees and the threats they face, this study aims to map organ forests and calculate the deforestation rate using remote sensing and machine learning tools. Specifically, we create organ tree maps for 2017/2018 and 2022/2023, comparing these maps to generate a map and calculate the deforestation rate over this period. Given the limited availability of organ-specific data, machine learning algorithms were selected for their efficiency, faster data analysis capabilities, and minimal computational requirements. This work seeks to provide specialists with valuable insights and statistics to inform strategies for mitigating organ forest deforestation.
## 5 Performance Analysis  
The performance analysis environment is shown Table 3 and original process's performance analysis is shown in Table 4. It was carried out on the LTE network components, UE, eNB and MME.  
All three initial attach processes include an initial attach with the IMSI process. The arithmetic operation of the encryption algorithm is carried out between the UE and the MME, and the eNB is used for passing through the communication process, so based on the initial attach with IMSI between the UE and the MME node, the average of overhead (time (s)), average delay (s) and average transmission rate (traffic received (bits/s)), the arithmetic operation of the encryption algorithm was measured and compared. In addition, for a performance analysis when the number of terminal users in the base station increases, the terminals were analyzed based on units of 1, 50 and 100 machine(s).  
Since the existing initial attach does not perform encryption, time (s) was excluded. It was found that the delay according to the encryption process in the proposed algorithm decreased a little (somewhat), and since there was no packet switching other than the initial access process, it did not have an impact on the transmission rate. Performance analysis for proposed process is shown in Table 5 and Summary of performance analysis is shown in Table 6.  
The maximum permissible delay in the LTE access network, based on the QCIdefined in 3 GPP TS 23.203, is 100 ms (0.1 s) for FTP and web browsing, 50 ms (0.05 s) for video streaming and 30 ms (0.03 s) for VoIP. Based on VoIP with the lowest delay (0.03 s, 100%), the overhead and delay turned out to be 35.3% and 32.0%, respectively, for the arithmetic operation of encryption, so it was found that the initial attach of the proposed algorithm had less overhead. Since the transmission rate dealt with the initial entry process only prior to the security setup, it turned out to have no impact.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Device**} & \multicolumn{3}{c}{**Original Initial Attach**} \\ \cline{2-4}  & **Time(s)** & **Delay(s)** & **Traffic Received (bits/s)** \\ \hline
1 & – & 0.004600 & 1.214 \\
50 & – & 0.007210 & 1.233 \\
100 & – & 0.010302 & 1.033 \\ \hline \end{tabular}
\end{table}
Table 4: Performance analysis: original process.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Element**} & **Values** & **Description** \\ \hline Cell & 1 & – \\ Number of UE & 1, 50, 100 & – \\ Test time & 9000 s & Initial data (0–150 s) not reflected \\ Initial Attach Attempt Cycle & 150 s & The UE service uses the initial attempt only \\ Portability Model & Deactivation & – \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance analysis environment.
# An Integrity Monitoring Method for Navigation Satellites Based on Multi-Source Observation Links
## 3 Design of Integrity Monitoring System with Multi-Source Observation Links
### Augmentation Parameters of Integrity with Multi-Source Observation Links  
LEO satellites as satellite-based monitoring stations can not only improve wide-area monitoring capability for navigation satellites but also be added to the resolution of navigation messages. Various research works on LEO orbit determination were carried out for the CHAMP satellite, GRACE satellite, Jason satellite, and other LEO satellites [9; 10; 11; 12], verifying that orbit accuracy of LEO satellites could reach centimeter level and LEO satellites could further augment the orbit accuracy of navigation satellite [17]. This paper proposes that the augmentation parameters of integrity should be solved with multi-source observation links to verify the effect of LEO satellites in indicating the accuracy of integrity.  
(1) Augmentation parameter of orbit correction  
Based on the precise point positioning model, orbit errors can be gained with kinematics equations and observation equations of navigation satellites and LEO satellites. Then, orbit errors could be smoothed with phase data to gain augmentation parameters of orbit correction.  
(2) Augmentation parameter of clock correction  
Based on pseudo-ranging and phase data of the observation equation, the augmentation parameter of clock correction could be gained as the coordinates of satellites and stations are fixed, and the clock of the main control station is chosen as the reference clock.  
(3) User differential ranging error  
User differential ranging error (UDRE) is the confidence limit value of the pseudo-range error, which is corrected by the augmentation parameters of orbit correction and clock correction and other errors, such as relativistic effect, tropospheric delay, antenna phase center bias, Earth tide, and multipath effect errors.
# A Power-Optimized Cooperative MAC Protocol for Lifetime Extension in Wireless Sensor Networks
## 5 Power-Optimized Cooperative MAC Protocol  
The above LETPO algorithm achieves the objective of optimizing the transmission powers of both the sender and its multiple cooperative nodes theoretically. However, during the execution of the algorithm, each node should transmit its channel gain and residual energy to the sender through a control packet. Then the sender performs the transmission power optimization algorithm with these parameters and declares the optimization results to the cooperative nodes with another control packet. Finally, the sender and cooperative nodes transmit the data packets by using their optimized transmission powers, i.e., the sender transmits the data packets using the optimized transmission power \(P_{\text{S}}\) and each cooperative node \(\text{R}_{\text{i}}\) relays the data packets using the optimized transmission power \(P_{\text{R}_{\text{i}}}\).  
To perform the LETPO algorithm efficiently, there are still two issues to be addressed. Firstly, if there are many potential cooperative nodes satisfying the constraint condition shown in Equations (9), (12), (13) and (16), how to select parts of them to transmit control packets to the sender, thus avoiding too much delay and energy overhead. Secondly, we should determine the transmission sequence of these control packets to avoid packet collisions.  
To address the above issues, execute the LETPO algorithm more efficiently and extend the network lifetime for WSNs, we propose the PO-CMAC protocol. As shown in Figure 2, the protocol includes three phases, namely, the channel reservation phase, the cooperative node selection phase, and the data packet transmission phase.
# Dual-Band Single-Layer Fractal Frequency Selective Surface for 5G Applications
## 4 Design Parameters and Guidelines
### Simulating in CST Microwave Studio  
Simulations were carried out using the frequency domain solver of the periodic structure workflow in CST. The simulations started with the FSS unit cell template and this ensured a correct basic adjustment of key simulation settings such as the boundaries and background. All simulations were carried out using unit cell boundaries, ensuring the three-by-three unit cell surface was considered. This allows us to include the effect of mutual coupling among adjacent cells, avoiding important mismatches when neglected. Ports Zmin and Zmax are automatically created by CST and their distance to the reference plane was not modified. The unit cell boundary settings enabled the Floquet boundary simulation mode, \(Z_{min}\), to be selected as the Floquet port [14]. The source type was plane wave. The frequency solver was set up so that frequencies from 0 to 6 GHz were taken into account. The frequency response plots in this paper represent \(S_{21}\) plots (in CST the \(S_{min(1)}\), \(Z_{max(1)}\) result) and \(S_{11}\) plots (in CST the \(S_{max(1)}\), \(Z_{max(1)}\) result) [13].  
Initial dimensions for just one unit cell were obtained adjusting the results in [11] for the desired resonant frequencies. Initial simulations were run for this single unit cell and then corrected by incorporating mutual coupling, as indicated previously. These initial dimensions were \(w=81\) mm, \(t=2.8\) mm, \(dx=3\) mm, \(dy=2\) mm. To obtain the later-explained Equations (1)-(4), parameter sweeps with varying values for \(t\), \(w\), \(dx\) and \(dy\) were performed. With each sweep, only one of the earlier mentioned parameters was varied in an interval. After this process, the final unit cell design was closed with the dimensions given in Table 1.  
Once the final design was obtained, a larger structure (50 cm by 50 cm) was also simulated in order to obtain additional insight on the impact of each element on the complete FSS. Figure 5 depicts the analyzed structure, whereas Figure 6a,b represent the electric fields induced on the surfaces at 800 MHz and 3.5 GHz, respectively. It can be observed that the lower frequency resonates with the larger overall bow tie structure and the higher frequency resonates with the smaller triangle cut-outs of the second iteration.  
The frequency response with the geometrical parameters provided in Table 1 is depicted in Figure 7. Both deep attenuation valleys are observed at target bands in this figure, which means that the design is able to fit the requirements for being used to mitigate (and almost block) 5G FR1 signals.  
Figure 5: A 50 by 50 cm FSS structure.  
Figure 6: E-field comparison. (**a**) Electric field distribution at 800 MHz. (**b**) Electric field distribution at 3.5 GHz.
## 7 Materials Used in Additively Manufactured Crash Boxes
### Fiber-Reinforced Polymers  
Polymers generally exhibit lower mechanical properties compared to metals. To enhance the performance of polymer structures, fibers, such as carbon fibers or glass fibers, are embedded within the polymer matrix [87]. This results in the formation of fiber-reinforced polymers that demonstrate improved mechanical properties [39]. A variety of studies have explored fiber-reinforced polymer crash boxes that are produced using AM techniques [88; 89; 90].  
[PERSON] et al. [91] investigated the energy absorption performance of additively manufactured thin-walled fiber-reinforced polymer tubes shown in Figure 14a. The tubes are manufactured using FDM with materials such as PA, short-carbon-fiber-reinforced polyamide (PACF), and short-glass-fiber-reinforced polyamide (PACF). Four configurations were examined: hexagonal, quadrangular, triangular, and circular cross-sections. The study found that the highest energy absorption performance was shown by the hexagonal tubes composed of PACF in both dynamic impact and quasi-static compression scenarios. Fiber-reinforced polymers are also influenced by temperature. [PERSON] et al. [92] examined the effects of loading rate and temperature on the crushing behavior of multi-cell hexagonal tubes (MHTs), as shown in Figure 14b. The specimens, fabricated using FDM with carbon-fiber-reinforced PA, were tested at four temperatures (\(-40\)\({}^{\circ}\)C, \(-10\)\({}^{\circ}\)C, \(20\)\({}^{\circ}\)C, and \(50\)\({}^{\circ}\)C) under both impact tests (an initial velocity of 10 m/s) and compression tests (constant rates of 10 mm/min, 100 mm/min, and 500 mm/min). The study found that the \(SEA\) of MHTs increases with compressive rates under compression but decreases when the loading condition shifts to impact. Regarding the effect of temperature, under compression, the highest \(SEA\) occurs at \(-10\)\({}^{\circ}\)C, with a decrease in \(SEA\) as the temperature rises. In contrast, impact tests demonstrate higher \(SEA\) at 50 \({}^{\circ}\)C compared to the lower temperatures.
# Deep Learning-Based Optical Camera Communication with a 2D MIMO-OOK Scheme for IoT Networks
## 5 Conclusions  
This paper proposes a monitoring system for Optical Camera Communication (OCC) based on a 2D MIMO-OOK scheme. The system utilizes a custom-designed spatial frame format with an LED array, allowing for rotation support--an essential capability for accurate 2D code recognition. To address frame rate variation and enable the reconstruction of large data packets from multiple images, each transmitted packet includes an SN. SNR measurements were conducted under different distances and exposure times in an indoor environment to examine the interdependence among some key parameters: distance, exposure time, and SNR. While increased exposure time can improve the SNR, this leads to a trade-off with reduced bandwidth, requiring precise calibration. In addition, by applying a deep learning model, our proposed scheme can support a mobile environment with YOLOv11 for LED matrix detection and a low BER with a deep learning decoder considering long distance and multiple users.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/electronics14153011/s1](https://www.mdpi.com/article/10.3390/electronics14153011/s1)]([https://www.mdpi.com/article/10.3390/electronics14153011/s1](https://www.mdpi.com/article/10.3390/electronics14153011/s1)). The supplementary material video shows the implementation of our proposed scheme at a distance of 20 m with 20 links.  
All authors contributed to this paper: H.N. proposed the idea and implementation methodology; and Y.M.J. supervised the work and provided funding support. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No.2022R1A2C1007884).  
Restrictions apply to the datasets due to the project policy.  
The authors declare no conflicts of interest.