esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Test Results
### Vibration Acceleration  
#### 4.2.1 Variation of Vibration Acceleration  
The acceleration and attenuation coefficient along the depth direction under the influence of various vehicle speeds for a 30 t axle load are shown in Figure 9.  
The trend in acceleration with depth exhibited similarities to the dynamic stress attenuation pattern, showing an exponential decrease with increasing depth. Furthermore, the acceleration decay coefficients exhibit a consistent trend, decreasing gradually with increasing speed along the depth direction. While dynamic parameters in the pavement layer experience rapid growth at higher vehicle speeds, the response level in the road-base layer, after considering the attenuation effect of the pavement layer, remains relatively small.  
The changes in acceleration and attenuation coefficients at various depths under different loads at the same velocity are depicted in Figures 10 and 11. Under the same velocity conditions, axle load exerts a noticeable impact on the acceleration level. As the axle load increases, the acceleration level rises, and the rate of decay decreases, resulting in an increased depth of the working zone. This observation highlights the dominant role of the axle load in the overall parameter variation when acceleration is employed as a control parameter to analyze the dynamic response pattern of the roadbed.  
Figure 10: Acceleration distribution along depth for different loads (\(\
u=90\) km/h): **(a)** Variation of the acceleration with the depth; **(b)** Variation of the attenuation coefficient with the depth.  
Figure 9: Acceleration distribution along the depth for different velocities (P = 30 t): **(a)** Variation of the acceleration with the depth; **(b)** Variation of the attenuation coefficient with the depth.  
The acceleration increment associated with varying load increments at different depths is illustrated in Figure 12. The figure reveals a nonlinear trend in the impact of load changes on acceleration. As the load increment increases, the acceleration growth demonstrates a corresponding acceleration in growth.
## 5 Evaluation
### Experimental Results  
#### 5.2.1 Fair Scheduling Strategy under Fixed Bandwidth  
To illustrate the advantages of FBAC in improving QoE fairness and overall QoE efficiency, we firstly evaluated the performance of the fair scheduling algorithm under the condition of fixed bandwidth. The fixed bandwidth scenarios where the bandwidth capacity of the bottleneck link is 1, 2, 4, and 6 (Mbit/s) were simulated by limiting the downlink bandwidth of the server. Specifically, three video clients of the same type competed on a shared bottleneck link and started playing their randomly selected videos from the video dataset simultaneously. We conducted 20 experiments for each comparison scheme and took the average as the final result.  
Figure 3a,b shows the performance of FBAC and the three comparison schemes on QoE fairness and QoE efficiency under different fixed bandwidths. It can be seen that, compared with other algorithms, the improvement in QoE fairness and efficiency of the FBAC algorithm is related to the capacity of the bottleneck link. In a higher-speed network link, the improvement of the FBAC algorithm becomes smaller, and the trend is more evident in the QoE fairness indicator. For example, when the bottleneck link capacity is 1 Mbit/s, the FBAC algorithm improves the QoE fairness of MPC by 9%, while the improvement is only 4% when the link capacity is 6 Mbit/s. This phenomenon occurs because increasing the video bitrate gradually reduces the impact of video content on video quality. When the video bitrate is 0.75 Mbps, the average difference in VMAF scores between videos was 10 points, and when the video bitrate is 2 Mbps, the difference was reduced to 5 points. This also shows that the QoE between users is fairer in a higher rate network link.  
In addition, although the increase in available bandwidth also reduced the room for improvement in QoE, when the available bandwidth was 6 Mbps, the overall QoE of FBAC was still 6 points, 3 points, and 4 points higher than that of MPC, Pensieve, and Bola, respectively. When the available bandwidth was 1 Mbit/s, it improved by 8-11 points. To better understand the significance of this improvement, the average difference in VMAF between 720 p and 1080 p video is 7.65 points.  
To illustrate how FBAC achieves better QoE fairness, we collect the sending rates and QoE scores of MPC and FBAC under a fixed bandwidth of 2 Mbit/s. As shown in Figure 4, since the underlying congestion control still constrains the ABR algorithm, the bandwidth is fairly allocated to each user, and the difference in video content will lead to unfair QoE among video users with the same available bandwidth. FBAC can perceive the difference  
Figure 3: Experimental results of fixed bandwidth. (**a**) QoE fairness; (**b**) QoE efficiency.  
in QoE between users and dynamically adjust the bandwidth allocation between video streams. This indirectly affects the decision of the ABR algorithm, thereby ultimately improving the QoE fairness among the video streams, as shown in Figure 5.
# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 11 Conclusions  
Our investigation demonstrates the effectiveness of Machine Learning techniques, particularly the Stacking Ensemble methods, for gamma/hadron segregation and energy estimation in the analysis of ASTRI Mini-Array data. The optimized compositions of these methods showed superior performance compared to the traditional Random Forest method. The proposed methods not only offer a performance on par with the standard methods implemented in A-SciSoft but also provide significant improvements in terms of energy bias reduction. These findings validate the robustness of the current reconstruction chain and open up possibilities for integrating high-performing Machine Learning techniques into the official data reduction and analysis pipeline of the ASTRI project.  
Although our study focused on simulated data specific to the ASTRI Mini-Array, we anticipate that the results can be qualitatively generalized to other Cherenkov telescope arrays, particularly the CTAO small-sized telescopes (SSTs). Future research should validate these findings using real data and explore the potential of incorporating temporal parameters and other Machine Learning methods, such as Deep Learning models and unsupervised learning techniques, which were not considered in this study. The ultimate goal is to enhance the performance of gamma/hadron discrimination and energy estimation in Cherenkov telescope observations and provide advanced tools for reconstructing real events acquired by the ASTRI Mini-Array.  
Conceptualization, A.P., G.C., A.L.B., V.L.P. and S.L.; Methodology, A.P., G.C., A.L.B., V.L.P. and S.L.; Software, A.P., V.L.P. and S.L.; Validation, A.P., G.C., V.L.P. and S.L.; Formal analysis, A.P., G.C., A.L.B., V.L.P. and S.L.; Investigation, A.P., G.C., A.L.B., V.L.P. and S.L.; Data curation, A.P., V.L.P. and S.L.; Writing--original draft, A.P., A.L.B., V.L.P. and S.L.; Written--review & editing, A.P., G.C., A.L.B., V.L.P. and S.L.; Supervision, A.P., G.C., A.L.B., V.L.P. and S.L.All authors have read and agreed to the published version of the manuscript.  
This research was funded by ASI INAF (2022-14-HH.0) and ICSC--Centro Nazionale di Ricerca in High Performance Computing, Big Data and Quantum Computing (European Union--NextGenerationEU).
###### Abstract  
Radio Determination Satellite Service (RDSS) is a characteristic service of BeiDou, which can provide users with short message communication services. Since the working frequency of an RDSS system is close to that of a 5G system, the RDSS system is very susceptible to interference from 5G out-of-band radiation. This paper analyzes the compatibility of 5G interference with an RDSS system from the perspective of the signal and the system. Firstly, the compatibility assessment is carried out from the perspective of the signal, the impact of interference on the capture and tracking performance of BeiDou is illustrated, and the safe coexistence distance of the two systems from the perspective of capture probability is obtained from the perspective of the signal. Subsequently, based on the link budget criterion, the interference of 5G base stations and 5G terminals to RDSS receivers under different frequency isolation and the required distance isolation for safe coexistence are analyzed from the system perspective. Finally, from the perspective of civil aviation safety, the aggregate interference is used as an evaluation index to evaluate the interference suffered by the aircraft during takeoff and landing and to obtain the interference suffered by the ground-based 5G base station during the takeoff and landing of the aircraft on different routes and in different 5G propagation environments. The simulation results show that when the airplane is closer to the ground, the ground 5G base stations will cause harmful interference to the RDSS receiver. In this study, the real flight data are combined with the simulation model to obtain the exact influence range of 5G interference on the RDSS system under different viewpoints.  
BeiDou radio determination service; 5G base station; interference analysis; civil aviation security 2024 11 522[[https://doi.org/10.3390/aerospace11/070522](https://doi.org/10.3390/aerospace11/070522)]([https://doi.org/10.3390/aerospace11/070522](https://doi.org/10.3390/aerospace11/070522))
## 4 Discussion on the Spatial Resolution
This implies that (1) the simulation is able to reproduce the experimental results where only electrons are involved in the final image, (2) the isotropic emission of the scintillation light introduces an additional blurring in the image, and (3) other effects not included in the simulation, such as electron diffusion and recombination, seem to have a little effect on the spatial resolution. It can also be concluded that even for an ideal triple-GEM detector, where only primary energy depositions are taken into consideration, a sub-millimeter spatial resolution is not reachable. Similar results were obtained for 30 kV X-rays. The edge response method underestimates the resolution of the detector as compared to the other methods. This effect can be caused by the electron mean free path in the gas that introduces an additional blur to the image.
# Communication Fault Maintenance Decision of Information System Based on Inverse Symmetry Algorithm
## 4 Discussion
### Information System Software Failure and Its Causes
#### 4.2.3 Password Faults and Causes  
The related problems existing in the operation of information system communication technology are also reflected in the application of passwords, because the settings of passwords are unreasonable, or the failure defects caused by the loss of passwords are relatively obvious, so it is easy to cause the occurrence of large defect failures, even the threat of information loss, which also needs to be paid enough attention. Combined with the failure of this kind of password, it has a close relationship with the relevant configuration of the whole system, especially in the setting of the link to retrieve the password, it is more likely to cause problems and affect the orderly operation of the system.
## 1 Introduction  
Every year, over forty million people worldwide receive nuclear medical imaging (Positron Emission Tomography (PET) or Single-Photon Emission Computed Tomography (SPECT) in the course of their treatment [1]. For many radiopharmaceuticals, the required radioactive isotopes can be produced on a medical cyclotron, using solid [2], liquid [3] and gaseous [4] target materials, enclosed in a metal vessel. Especially in the gas and liquid phase on low energy cyclotrons (up to 24 MeV), the achieved radionuclide yield is often lower than predicted by nuclear cross sections [4, 5, 6]. Other effects like target body and transfer line material, density reduction due to temperature increases during irradiation, convective currents and phase changes may have an effect on the recoverable radionuclide yield [5, 6, 7, 8]. In recent years, several modeling approaches have endeavored to study these aspects [3, 4, 7, 9, 10, 11, 12], but meaningful comparison between experiments is hindered by the lack of observables in the target as reliable and localized temperature and pressure measurements are difficult to conduct. Most often, the pressure is measured either in the target body instead of the actual target gas or liquid [13] or on a line connected to the target volume [5]. As the pressure transducer may be located at a distance of several meters away from the target, its reading may not necessarily reflect the true conditions in the target.  
At this point, simulations that can predict the temperature and pressure fields are being developed [3; 4; 7; 9; 11; 12], but physical measurements of both parameters are needed for validation. Calculations are complicated by the fact that the proton deposition in the target medium affects the target density, which in turn causes a pressure gradient and convective currents. This density gradient can affect the radioactive yield of the isotope being produced.  
To address these issues, we investigated new sensors to measure the local temperature and pressure in an isotope production target. We characterized the response of an optical fiber with a Bragg grating to radiation fields. Before exposing the fiber to significant proton-beam current and elevated pressure and temperature in an isotope production target, tests were carried out at lower doses and dose rates at a dedicated proton and neutron test facility to establish the radiation hardness of the bare, unpackaged sensor. After confirming the durability in those radiation fields, we developed functional temperature and pressure sensors and inserted them into the cavity of an isotope production target to measure the circulating fluid temperature and pressure during irradiation.
## 4 Experiment and Discussion
### Winding Angle Experiment  
Before starting the experiment, the relative angle between the camera and the axis is adjusted so that the camera is horizontal to the radial direction of the axis when taking images, the camera object distance is set to 550 mm, and the light source brightness is set to 347 lux. Eight coil images were dynamically captured during machine production, and the ROI was set to 640 \(\times\) 480 pixels, where (a-d) is the ROI when the wire with a 2.4 mm wire diameter is wound, and (e-h) is the ROI when the wire with a 2.6 mm wire diameter is wound. The pre-processed images are shown in Figure 21, and the detection results of each ROI are shown in Figure 22.  
Figure 19: Shape center coordinates of guide wire profile.  
Figure 20: Traverse outline recognition result.  
Figure 21: ROI images during production: (**a–d**) is the ROI of the 2.4 mm wire; (**e–h**) is the ROI of the 2.6 mm wire.  
Figure 22: _Cont._  
In each image of Figure 22, the horizontal coordinates represent the number of valid wires detected from left to right in the ROI, and the vertical coordinates represent the angle range. Since the coils are wound back and forth from the wire layout device, the winding angles formed by the adjacent layers are relative. The red dots are the angles detected by the proposed method, and the blue dots are the angles measured by our electronic digital display angle scale. From the results in each figure, the maximum difference between the two methods is 0.3\({}^{\circ}\), which is within our allowable error, and with a fixed amount of feed per wire turn in actual production, this winding angle has a small range of variation, so the final results are all around plus or minus 1\({}^{\circ}\). However, there are fluctuations in the individual results, and some of the wire numbers do not match the actual conditions. The reason for these errors is that during the winding process, the uneven wire surface and some gums in the gaps produce light reflection, which affects the accuracy of the skeleton identification, which in turn leads to the deviation of the identified individual features from the straight line, but did not affect the overall result analysis, and the usability of the identification winding angle method was proved after doing a full comparison.
# Development and Field Testing of a Wireless Data Relay System for Amphibious Drones
## 2 Materials and Methods
### Water Quality Monitoring System with Amphiious Drones
The external (third-party) header files used in the ArduinoTM sketches (programs) were SparkFunBME280.h (SparkFun Electronics Inc., United States) for the atmospheric sensor, OneWire.h ([PERSON]) and DallasTemperature.h ([PERSON]) for the water temperature sensor, and XBee.h ([PERSON]) for the XBeeTM (Zigbee, Chaska, MN, USA) wireless communication chips.
# A Circle Center Location Algorithm Based on Sample Density and Adaptive Thresholding
## 2 Theoretical Design of CLSDAT
### Calculating the Center Coordinate with Different Sample Density Iteration  
The density of the circle center samples, denoted as \(\rho\), is used to measure the number of circle center coordinates within a unit area. It is defined as shown in Equation (6).  
\[\rho=\frac{T}{A} \tag{6}\]  
where \(A\) is the area of the statistical region in pixels, and \(T\) is the number of circle center coordinates in that region.  
In a circle center sample data set, as shown in Figure 4, most of the samples are concentrated near the true center due to the imperfect alignment of the contour points on the circular contour. This region exhibits high density, while other regions have lower sample densities.  
In each iteration, an optimal circle center can be determined. With this optimal center as the reference, different sample densities are selected based on the distance from the optimal center. The width of the kernel function is automatically determined based on the number of sample points. This process generates a set of circle center coordinates, as shown in Figure 5. The standard circle used in Figure 5 has a center coordinate of (255, 255). It can be observed that the iteration results quickly converge to the standard center.
## 2 Numerical Modeling  
### Contact Model  
The translational and rotational motions of a particle with a constant mass (\(m\)) could be expressed by [PERSON]'s second law as follows:  
\[m\frac{du}{dt}=\sum\mathbf{F}_{c}+\sum\mathbf{F}_{b}+m\mathbf{g} \tag{1}\]  
\[l\frac{d\mathbf{\omega}}{dt}=\sum\mathbf{M}_{c}+\sum\mathbf{M}_{b} \tag{2}\]  
where the subscripts \(c\) and \(b\) represent the contact and the bond, respectively. The mass (\(m=\rho h_{d}\pi r^{2}\)) is calculated as the disk-shaped particle that has a certain thickness (\(h_{d}\)). \(\mathbf{u}\) is translational velocity of the particle. \(\mathbf{F}_{c}\) and \(\mathbf{F}_{b}\) are the contact force and the bond force, respectively. \(m\mathbf{g}\) is the gravitational force. \(l\) is the inertia moment of the particle, and \(\mathbf{\omega}\) is the angular velocity of the particle. The particle rotation is calculated by the sum of the torque (\(\mathbf{M}_{c}\)) caused by collisions and the moment (\(\mathbf{M}_{b}\)) by the bond that transferred to the particle.  
The contact force (\(\mathbf{F}_{c}\)) by the collisions between particles could be simplified using a spring-dashpot model. The contact force consisted of the normal (\(\mathbf{F}_{c,n}\)) and tangential (\(\mathbf{F}_{c,t}\)) forces, which could be expressed as  
\[\mathbf{F}_{c,n}=k_{n}\delta_{n}-\gamma_{n}\dot{\delta}_{n} \tag{3}\]  
\[\mathbf{F}_{c,t}=\min\left(\left|k_{t}\int_{t_{c}}^{t}\Delta\dot{\delta}_{t}dt- \gamma_{t}\dot{\delta}_{t}\right|,\ \mu\mathbf{F}_{c,n}\right) \tag{4}\]  
where the subscripts \(b\) and \(t\) represent normal and tangential components, respectively. \(k\) and \(\gamma\) are the spring stiffness and the damping coefficient, respectively. \(\delta\) and \(\dot{\delta}\) represent the overlap and the relatively velocity between two contact particles, respectively. The normal overlap (\(\delta_{n}\)) was a function of the particle radius and the distance between the particle centroids at the current time step. The tangential overlap (\(\delta_{t}\)) was calculated by the incremental distance in the tangential direction after an incidence of contacting. The maximum tangential force was limited by the Coulomb friction law with the friction coefficient (\(\mu\)) and normal contact force (\(F_{c,n}\)). The spring stiffness and damping coefficient were dependent on the particle properties and selected contact law. The contact law could be divided into a linear contact law (Hook contact model) and a non-linear contact law (Hertz contact model). In this study, [PERSON] contact model was used [30].
## 3 Case Study: LCA EoL of SRC and CRC
### Life Cycle Inventory  
Conservative approaches are used in the selection of processes and in the assumptions, when not given as primary or clearly defined literature data. German datasets--the double wall is assumed to be demolished and processed in Germany--are used when possible; where this is not feasible, European (first) and global (second) datasets are used.  
The life cycle inventory (LCI) for CRC is based on the processes outlined in Section 2, including the energy and fuel needed for demolition (from wall to small re-usable particles), transport routes of ~100 km, and the mechanical recycling or pyrolysis. The energy values and transports, including fuel, refer exclusively to the FU. The entire LCI is based on literature data or datasheets from German companies, which are converted to the target value, according to justified assumptions (Figure 3).  
For SRC, we assume three different demolition scenarios: (A) a mobile plant at the demolition location, (B) a stationary plant with a conventional processing (dry preparation), and (C) a stationary plant with innovative processing (dry and wet preparation). For SRC, a distance of ~100 km for each transport is assumed. Figure 4 shows the modelled flow chart, starting from the selective demolition on site (Scenario A) or a stationary plant (Scenario B1 and B2). The steel reinforcement is unpacked, meaning it can be ready for recycling at any steel-producing site. The concrete can be demolished into different sizes of reusable material. Flows entering the scope of the assessment are fuel (diesel), electricity, and water.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Carbon-Reinforced Concrete** & **Amount** & **Unit** & **Steel-Reinforced Concrete** & **Amount** & **Unit** \\ \hline Total weight & 1.43 & t & Total weight & 2.86 & t \\ \hline Total double wall & 0.6 & m\({}^{3}\) & Total double wall & 1.1 & m\({}^{3}\) \\ \hline Concrete per double wall & 1.42 & t & Concrete per double wall & 2.63 & t \\ \hline Carbon scrim per m\({}^{3}\) & 0.0170 & t & Steel scrim per m\({}^{3}\) & 0.2100 & t \\ \hline Carbon scrim per double wall & 0.0102 & t & Steel scrim per double wall & 0.2310 & t \\ \hline Carbon scrim per wall & 0.0051 & t & Steel scrim per wall & 0.1155 & t \\ \hline \hline \end{tabular}
\end{table}
Table 1: Volume and weight of concrete and reinforcement (according to [58]).  
Figure 2: FU: re-usable material out of carbon fiber vs. steel-reinforced double wall analyzed in the study.  
Figure 4: Flow Chart: EoL SRC.  
Figure 3: Flow chart: EoL CRC.
# Federated Learning for Secure In-Vehicle Communication
## 5 Results Analysis
### Network Traffic Overhead  
Figure 18 illustrates the amount of network traffic sent and received by the FL server during the training. We can notice that:  
* Network traffic increases with the number of epochs for both models. A possible explanation for that is more training per round leads to larger or more frequent updates from clients.
* The communication patterns of both models remain generally comparable. However, the a-BiLSTM shows lower or similar data transfer volumes in 1 epoch and 5 epochs settings, which proves that our model has comparable communication demands despite its complex architecture, where the observed upper bounds on data transmission when using LSTM achieves values in the order of \(10^{8}\) bytes.