esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring Carbon-Fiber UAV Structures as Communication Antennas for Adaptive Relay Applications
## 5 Conclusions  
The experimental evaluation of the carbon fiber antenna integrated into a UAV copper frame confirmed a generally good agreement with theoretical predictions while also highlighting minor discrepancies attributed to environmental influences, material imperfections, and measurement uncertainty.  
VSWR and reflection coefficient (S11) values remained close to expected levels, confirming that the antenna maintains proper impedance characteristics for practical use. Minor deviations in measured gain and received power--within 0.04 dB and 1.3 dB, respectively--were consistent with predictable factors such as near-field effects, reflections, or slight misalignments.  
The radiation pattern in both azimuth and elevation planes matched theoretical expectations, though secondary lobes appeared with small amplitude deviations. The sensitivity of performance to polarization mismatch was strongly confirmed, with a 90\({}^{\circ}\) misalignment resulting in a 15 dBm loss in received power. Likewise, environmental obstructions (e.g., metallic objects or human movement) introduced measurable drops of 9-18 dB, emphasizing the importance of clear line-of-sight conditions.  
Measurements performed using the two-way model setup demonstrated the impact of ground reflections on signal strength, reinforcing the need to consider multipath propagation in real environments.  
In conclusion, the carbon fiber structure of the UAV can successfully act as a directional antenna, but its performance depends critically on alignment, polarization, and propagation conditions. Experimental validation proved essential, and further improvements--particularly in impedance matching and material optimization--could enhance communication efficiency for UAV applications.  
Conceptualization, C.V., A.A., L.G., G.C. and M.N.; methodology, A.A.; software, A.A.; writing--original draft preparation, A.A., G.C. and M.N.; writing--review and editing, A.A., L.G. and G.C.; supervision, C.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are available upon request from the corresponding author. The data are not publicly available due to the IPR agreement signed by the authors with the funding institution. All data to be made public must undergo the institution's internal check and approval.  
Fund of the experimental work presented in this study was carried out at the Royal Military Academy in Brussels. The authors would like to express their gratitude for the support and facilities provided by the institution.  
The authors declare no conflicts of interest.
# Raw Material Flow Rate Measurement on Belt Conveyor System Using Visual Data
## 3 Methodology  
The section introduces the proposed framework to estimate the flow rate of limestone and coke being transported from the belt to the top of the Kiln. A visual sensor mounted on top of the conveyor belt is utilized to capture the entire conveyor belt region. This data is used to assess the stone's quality, the ratio of coke and limestone, issues of belt conveyor, etc. The proposed framework starts by extracting the region of interest to capture only the conveyor belt and discard any unnecessary area around it. Afterward, pre-processing is done to remove any noise from the video frames. Later, considering the challenges of an outdoor environment, limited data, and a single camera, we devised our algorithm to properly segment the material transferred through the conveyor belt. Finally, features are extracted from the segmented region, and machine learning is used to find hidden patterns in the features and estimate the flow rate of the load. The complete process can be visualized in the conceptual map in Figure 3. Each step is described in the following sections.  
Figure 2: Variation in dataset video quality: good image quality (**a**), captured at evening time (**b**), and dusty image (**c**).  
The implementation of the proposed methodology is done in python by utilizing several libraries for various tasks. Mainly, OpenCV is used for image processing tasks. In addition, Pandas, Numpy, and Scikit-learn are utilized for data analysis and machine learning. Finally, Jupyter Notebook is used as a development environment for better documentation and quick analysis.
# Impact of Mega Constellations on Geospace Safety
## 3 Impact of Starlink on Environmental Fluxes in Earth's low-orbit Space
becomes concentrated, mainly in the range of 482-510 km, 210-230 kg, and the maximum value is higher and varies greatly. The maximum value becomes 504.2253 km, 222.6219 kg at 4.257 \(\times\) 10\({}^{-5}\)/m\({}^{2}\)/yr, 1460.878 times that of the former, considering the influence of Starlink before and after on the flux variation. Similar variation trends for Alt-V and Alt-Mass indicate that there is a difference in the influence of different satellite characteristics on the satellite flux variation and that the flux variation caused by the change in inclination angle is distinct from the flux variation caused by the change in volume and mass. The effect of inclination angle on flux is more significant, and it also demonstrates that the effect of Starlink inclusion on flux variation of LEO satellites is evident. Some satellites with more special characteristics are more dangerous in this situation, implying that the choice of some mission orbits becomes more dangerous in the face of the complex LEO space situation, and there even be a situation where the orbit is no longer viable. Figure 6: _Cont._  
The Starlink constellation has a significant impact on the Earth's low orbit environment from a flux perspective. To exclude the unique characteristics of the satellites themselves, which would render the results unreliable, three target satellites with distinct characteristics were chosen for analysis and calculation. Mass and Alt-V flux variation data allowed us to calculate the ACPL values of the three target satellites and the corresponding number of avoidance operations. The specific data are provided below. It should be noted that, despite the drastic flux variation, the impact of flux variation on a target satellite's collision probability may be different due to the target satellite's volume and mass, and this difference will be reflected in the impact on its ACPL. Figure 6: Object height fluxes and object size fluxes. (**a,b**) Before and after Alt-Mass fluxes of GAOFEN satellites; (**c,d**) Before and after Alt- Mass fluxes of CORVUS BC5 satellites; (**e,f**) Before and after Alt- Mass fluxes of YAOGAN-35C satellites.
## 3 Results
### Phase 1--Initialization  
In the initialization phase, the computational model was adjusted to optimize its performance under local conditions of the in-situ experiment. In this phase, the interior surface temperature \(T_{\rm s}\) was the subject of experimental-computational analysis. The procedure of fitting simulated data \(T_{\rm s,sim}\) to those determined experimentally; \(T_{\rm s,exp}\), was used to adjust the heat transfer coefficient \(\alpha\), the free parameter of the model describing the rate of heat exchange on the solid/gas interface. The entire procedure was driven by searching for the minimum value of the Root-Mean-Square-Error (\(RMSE\)) between experimentally determined and simulated surface temperature variations. For the model adjustment, two periods were selected for demonstration. As mentioned in Section 2.2, the first adjustment (compulsory) was done on the dataset collected at the very beginning of the experiment in the period between 11 August 2020 and 24 August 2020 (Day 1-Day 14 of the experiment), in which 308 hourly outputs constituting time-resolved surface temperature variation were analyzed. The second adjustment of the computational model (optional) was done in the period between 1 February 2021 and 7 February 2021 (Day 175-Day 181 of the experiment, 169 hourly outputs) to compensate for the effects of a long-term measurement by adapting the model to the potentially new conditions that might be differing from those at the beginning of the experiment.  
The \(RMSE\) of temperature profiles vs. heat transfer coefficient function is shown in Figure 7. The heat transfer coefficients in these particular periods were determined as \(\alpha_{1}=10.75\) W\(\cdot\)m\({}^{-2}\cdot\)K\({}^{-1}\) and \(\alpha_{2}=13.75\) W\(\cdot\)m\({}^{-2}\cdot\)K\({}^{-1}\), respectively, indicating that environmental conditions slightly changed during the measurement. Therefore, the reinitialization should bring an increased accuracy to the subsequent re-adjustment procedures and experimental measurement, eventually.  
The computational model performance during the ongoing experiment is presented in Figure 8. Based on the visual comparison of simulated temperature, \(T_{\rm s,sim}\), and experimentally determined temperature, \(T_{\rm s,exp}\), the computational model setup can be considered appropriate for further use in this research, and the optimized heat transfer coefficients could be used for the subsequent heat flux analyses using experimental data. The need for performing the re-initialization is documented by the difference in average indoor temperature in Initialization 1 and 2 which is more than 20 \({}^{\circ}\)C. Also, the daily oscillation of indoor temperature during Initialization 1 is apparent, which underlines the necessity of re-adjustment of the computational model to address different experimental environments.
# The Efficient Processing of Moving \(k\)-Farthest Neighbor Queries in Road Networks +
## 7 Empirical Evaluation
### Empirical Results
Again, MOFA is four times faster than the conventional algorithm. The query execution times are stable and independent of the \(k\) value. Figure 14(c) presents the query execution times for \(2\leq Q_{F}\leq 20\). MOFA is 1.0, 2.0, 4.2, 5.2, and 10.3 times faster than the conventional algorithm when \(Q_{F}=2,\ 4,\ 8,\ 10,\) and 20, respectively. Figure 14(d) presents the query execution times for \(1\leq|C|\leq 10\). MOFA is up to four times faster than the conventional algorithm. Figure 16 presents the performance of MOFA and the conventional algorithm on the SF road network. Figure 15(a) presents the query execution times for \(10^{3}\leq|F|\leq 5\times 10^{3}\), indicating that MOFA outperforms the conventional algorithm by up to four times when \(|F|=5000\). Figure 15(b) presents the query execution times for \(1\leq k\leq 16\), indicating that MOFA outperforms the conventional algorithm by up to four times in terms of query execution time. The query execution times are stable and independent of the \(k\) value. Figure 15(c)  
presents the query execution times when \(2\leq Q_{F}\leq 20\), indicating that MOFA is 1.0, 2.0, 4.2, 5.2, and 10.3 times faster than the conventional algorithm when \(Q_{F}\)=2, 4, 8, 10, and 20, respectively. Figure 16d presents the query execution times for \(1\leq|C|\leq 10\), indicating that MOFA is up to 4.2 times faster than the conventional algorithm when \(|C|=10\). In summary, MOFA is faster than the conventional algorithm in all cases. In particular, the difference in performance between MOFA and the conventional algorithm increases with the query frequency. This confirms that MOFA benefits from the rapid retrieval of candidate facilities at the border points of the query segment and from the computation of valid segments for the query segment. Figure 16: Performance comparisons between MOFA and the conventional algorithm on the SF road network: (**a**) \(10^{3}\leq|F|\leq 5\times 10^{3}\); (**b**) \(1\leq k\leq 16\); (**c**) \(2\leq Q_{F}\leq 20\); (**d**) \(1\leq|C|\leq 10\). Figure 15: Performance comparisons between MOFA and the conventional algorithm on the NA road network: (**a**) \(10^{3}\leq|F|\leq 5\times 10^{3}\); (**b**) \(1\leq k\leq 16\); (**c**) \(2\leq Q_{F}\leq 20\); (**d**) \(1\leq|C|\leq 10\).
## 5 Parametric Studies on Energy Dissipation
### Excitation Frequency Effects  
For a constant 50 kN excitation, frequencies of 25 Hz, 30 Hz, 35 Hz, and 40 Hz were analyzed. Figure 13 presents void ratio versus time plots for these different frequencies. It was observed that all samples underwent monotonic densification, with void ratios decreasing slightly faster at higher frequencies. However, the discrepancies in void ratio reduction were minor within identical loading durations. Therefore, the excitation frequency did not significantly affect the effectiveness or efficiency of S-RM compaction.  
Figure 12: Energy dissipation pathway under different excitation forces: (**a**) dashpot energy; (**b**) sliding energy; (**c**) rolling slip energy; and (**d**) strain energy.  
Figure 13: Changes in void ratio with time under different excitation frequencies.  
Figure 14 displays the evolutions of the different dissipation components. In contrast to strain energy, which remained unaffected by frequency, dashpot, sliding, and rolling slip energy progressively increased with higher frequencies under the same loading period. This can be attributed to the fact that increased frequencies result in more loading cycles, leading to greater energy consumption. However, since strain energy depends on overall deformations during the given timeframe, the differences in its evolution across frequencies are negligible.  
In summary, increasing excitation forces can significantly improve the degree of compaction up to a certain limit. However, it also leads to a substantial increase in energy consumption. On the other hand, adjusting the frequency within a typical range has a mild effect on compactness, without appreciable effects on energy dissipation. These trends provide valuable guidance for achieving target densification levels while optimizing energy inputs.
## 2 Materials and Methods
### Quantitative Reverse Transcription Polymerase Chain Reaction (RT-qPCR) Assay  
The mRNA levels of the cellular antioxidant system-related genes (_SOD1_--superoxide dismutase 1, _SOD2_--superoxide dismutase 2, _GSH_--glutathione reductase, _CAT_--catalase, _GPx1_--glutathione peroxidase 1, _MGST1_--microsomal glutathione S-transferase 1 and _NFE2L2_--nuclear factor erythroid 2-related factor 2) and apoptosis-related genes (_BAX_--BCL2 associated X, apoptosis regulator, _BCL2_--B-cell lymphoma 2, _BCLXL_--B-cell lymphoma--extra large, _CASP2_--caspase 2, _CASP3_--caspase 3, _CASP9_--caspase 9, _FAS_--Fas cell surface death receptor, _FASL_--Fas ligand, _TNF_--tumor necrosis factor and _TNFR1_--tumor necrosis factor receptor 1) were assessed using the quantitative reverse transcription polymerase chain reaction (RT-qPCR) technique using SYBR Green 1 chemistry (SYBR Green QuantiTect RT-PCR Kit; QLAGEN, Valencia, CA, USA) and an Opticon(tm) DNA Engine Continuous Fluorescence Detector (MJ Research, Watertown, MA, USA). All of the samples were tested in triplicate. In addition, \(\beta\)-actin was the endogenous control and wells without the RNA template were the negative control. The oligonucleotide primers and the RT-qPCR thermal profile were previously reported by [PERSON] et al. [22] and [PERSON] et al. [23]. The results were performed as the copy number mRNA/\(\mu\)g RNA.
## 6 Simulation and Analysis  
### Load and Climate Overview  
This study focuses on a building cluster within the Shanghai Economic Zone as the load terminal for the analysis. The cluster comprises office buildings, hotels, and commercial centers, with the commercial center spanning 26,000 square meters, the hotel covering 15,000 square meters, and the office buildings occupying an area of 36,000 square meters. The electricity, heat, and cooling demands of the building cluster in 2019 are simulated and modeled using Dest 2.0 software. Considering the seasonal and climatic characteristics of Shanghai, the entire year's load is segmented into three typical days: summer, winter, and transitional seasons, utilizing the K-means clustering method. Additionally, to compare the output levels of solar and wind energies, the performance coefficients of photovoltaics, solar collectors, and wind turbines are calculated based on the solar radiation, ambient temperature, and wind speed on typical days in Shanghai.  
Given the location of the building cluster in the Shanghai Economic Zone, its load demand is closely tied to the GDP. Therefore, the three scenarios of rapid, steady, and green developments, the average annual load demand growth rates for the years 2020-2040 are set, as indicated in Table 7.
## 1 Introduction
(2016) proposed a multiobjective optimization method, combining the efficient global optimization (EGO) and multifidelity hybrid surrogate models, for optimizing helicopter blades. The study showed that both the computation cost and optimization accuracy were improved. More specifically, that the optimization approach based on the Kriging surrogate model and genetic algorithm is suited for complex engineering optimization problems, particularly those with strong nonlinearity. For example, in order to improve the riding quality of a rail vehicle, [PERSON] (2016) and [PERSON] (2016) applied this approach to optimize rail vehicle suspension parameters, which solved the problem of low optimization efficiency. Furthermore, [PERSON] et al. (2017) employed this approach for airfoil structure optimization, aiming to obtain better aerocraft aerodynamic performance, while taking into consideration the strong nonlinearity during aerodynamic analysis. Considering the time consumption and strong nonlinearity performance of an automobile's collision deformation analysis, this optimization approach has been also used to optimize the front structure for an automobile ([PERSON] et al., 2017). These data indicate that the optimization approach based on the Kriging surrogate model and genetic algorithm is an effective method for complex engineering optimization with a high computational cost and strong nonlinearity, which could also be introduced to VX gasket sealing structure optimization. In this paper, we intended to present a multiobjective optimization approach for the VX gasket structure based on the Kriging surrogate model-NSGA-II algorithm. We also considered the randomness of the working load, in which the Kriging surrogate models of the sealing performance are constructed to accelerate the optimization efficiency. A random finite element (R-FE) model of the subsea connector was established to account for the randomness of the working load during the responses calculation of the samples for the Kriging surrogate models. In addition, the second generation of the non-dominated sorting genetic algorithm (NSGA-II) was used to determine the optimum solution for the VX gasket structure.
## 4 Implementation of Proposed Architecture
### Function-Level Description of MEC/Cloud Orchestrator  
Each of the functions of the GUI and the Orchestrator is explained below in detail. The GUI function consists of the GUI View function and the Subscription Management function. The Orchestrator function consists of six parts: the Service Query function, Service Registry function, Database Update function, Image Registry function, Deploy Judgments function, and Setup Execution function. The GUI View function allows end users to connect to a wide-area wireless network provided by the Legacy Telecom Operator via the control plane using an HTTP/HTTPS. After accessing the GUI, the end-user begins to use it by registering a subscription (billing) with a secure connection. Here, the user management access information and subscription management used by the end user at the time of access by the GUI function are performed by the Subscription Management function. In addition, the application subscription information is managed by the Subscription Management function. On the other hand, the GUI View function functions as a request received on the Web and a transaction role for each part. Requested information for application requirement requests received by the GUI View function are sent to the Orchestrator function via Southbound Interface (e.g., Restful API, Curl, SSH, CLI). Then, the end user can request application deployment according to subscription payment. Meanwhile, the Orchestrator function regularly monitors the resource usage/allocation of cloud and MEC for computing/network/storage and the Database Update function updates/manages on Database based on monitoring info. The Service Registry function registers application deployments requested by the Service Query function. The application selected here starts the deployment process using either the one registered in the ImageRegistry function provided by the Third Party in advance, or the cached content generated by the learning function by AI/ML. When starting the deployment process, the Deploy Judgments function is determined by logic based on the scenario in which either cloud or MEC (e.g., the physical location) possessed by the Private/Local Telecom Operator is registered. Examples of registered strategies include minimum latency, cost, and processing performance. Finally, the Setup Execution function executes the Config set in the virtual machine or container remotely when the deployment is completed. As a result, the end user can receive the service from the deployed application via the user plane.
# Blind Detection for Serial Relays in Free Space Optical Communication Systems
## 2 System Model
### Channel Model  
A log-normal channel for both weak and moderate turbulence is used. The log-normal distribution for the channel coefficient is defined as [14]  
\[f_{LN}(h_{a})=\frac{1}{h_{a}\sqrt{8\pi\sigma_{x}^{2}}}\exp\left(-\frac{\left( \ln(h_{a})-2\mu_{x}\right)^{2}}{8\sigma_{x}^{2}}\right),\,h_{a}>0 \tag{2}\]  
with \(h_{a}=\exp(2x)\), where \(x\) is normally distributed random variable with mean \(\mu_{x}\) and variance \(\sigma_{x}^{2}\). The channel attenuation can be modeled as [13]  
\[h_{l}=\frac{A_{r}}{\pi\left(\frac{\theta\,d_{0}}{2}\right)^{2}}\exp(-\beta_{0 }d_{0}) \tag{3}\]  
where \(A_{r}\) is the aperture area, \(\theta\) is divergence angle, \(d_{0}\) is the distance between the transmitter and the receiver and \(\beta_{0}\) is extinction coefficient.