esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Recent Research into the Causes and Control of Noise during High-Speed Train Movement
## 2 High-Speed Train Noise Composition
### High-Speed Train Mechanical Vibration Noise  
#### 2.2.1 Braking Noise  
Brake squeal noise is loud and associated with high sound pressure and a sharp tone, which poses a hazard to people's physical and mental health. The friction of the brake disc generates brake squeal noise, friction generates wear, and the generation and development of brake noise are inseparable from frictional wear behavior.  
In their study, [PERSON] et al. [25] proposed that wear of the braking surface leads to significant randomness in braking noise spectra. [PERSON] et al. [26] suggested that the uniform friction layer of the braking interface exerts an important influence on the occurrence of squeal noise. [PERSON] et al. [27] estimated the effect between the surface morphological characteristics of the braking interface and the braking noise. [PERSON] et al. [28] discovered that material debris generated changes the friction coefficient and thus affects frictional noise. [PERSON] et al. [29] found that when frictional squeal noise is generated during braking, a large amount of debris tends to accumulate on the material surface.  
The aforementioned research found that high-speed train brake noise and tribology are inseparable, and now with the continuous improvement of materials and production processes of the train brake disc, frictional brake noise is further reduced.
# Design and Implementation of a Passive Optical Network for a Small Town +
## 3 Methodology
### Traffic Aggregation and PON Dimensioning
In the context of 32 users per PON, the aggregate demand is naturally lower (e.g., 32 \(\times\) 50 Mbps = 1600 Mbps, about 64% of capacity). With 64 users, the system operates with a higher oversubscription (e.g., 64 \(\times\) 50 Mbps = 3200 Mbps potential, requiring statistical sharing of the 2488 Mbps). This level of oversubscription (~1.3:1 for 50 Mbps tiers) is within acceptable bounds for GPON networks, given typical user behavior [18]. Additionally, the use of IP multicast for IPTV means that popular live channels are delivered once on the downlink and replicated to users at the splitters \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network Segment** & **Split Ratio** & **Number of Users** & **Assumed Avg. Active Traffic** & **Aggregated Bandwidth** \\ \hline Per-building & & & & \\ splitter & 1:16 & 16 users & \textasciasci{}10 Mbps per user & \textasci{}160 Mbps on that branch \\ (second level) & & & & \\ \hline Feeder segment & & & & \\ (first level, total) & 1:4 (4 splitters \(\times\) 16 users) & 64 users total & \textasci{}10 Mbps per user & \textasci{}640 Mbps toward OLT \\ \hline GPON PON & & & & \\ Port Capacity & & up to 64 users & Up to \textasci{}39 Mbps per user (simultaneous) & 2488 Mbps downstream \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Typical network segments, split ratios, number of users, assumed average active traffic and aggregated bandwidth. or ONTs, substantially reducing duplicate traffic when multiple homes watch the same channel. This multicast efficiency further lowers the effective bandwidth demand on the PON for IPTV services. Thus, by carefully planning the service mix and factoring in realistic usage patterns, the GPON downstream (2.5 Gbps) and upstream (1.25 Gbps) capacity was deemed sufficient for the small town's peak aggregated traffic.
## 6 5G as a Smart City Enabler  
5G, with its advanced features, as discussed in the previous section, meets the demands of smart city services and applications. The 5G capabilities that enable large-scale IoT implementation for smart cities are [21; 22; 23]:  
* Device connectivity: 5G supports a significant increase in the number of interconnected devices, such as sensors, cameras, and actuators, in a wireless infrastructure, which is the basis of IoT deployment. These devices are required in homes, roads, traffic hubs, and public places, such as bus stops and railway stations, in order to support smart traffic systems, smart homes, and public safety, as well as the security and surveillance requirements of smart cities. The interconnection of these devices is made possible by the very large increase in the capacity of the 5G system, due to the following: (a) Optical conversion of the backhaul/core of the network with the deployment of fiber optics in cities; (b) The usage of more antenna systems with lower transmit power per antenna; (c) Therefore, more cells for a given urban coverage area; (d) Thus, serving more users within each cell and, therefore, across all cells in total. These users are no longer only physical users--subscribers to the mobile service with their mobile phones--but also virtual users, i.e., smart devices that can be served with multi-adaptive rates of broadband connectivity by 5G antennas (i.e., base stations).
* Very high bandwidth up to 100 MHz to support video streaming services over wireless channels and high volumes of data;
* Ultralow network latency for improved user experience, including delivery of 3D and holographic images, for applications such as unmanned driving and AR/VR applications. The latency of less than 1 ms provided by 5G enables ultralow-latency communications that can support sensitive applications in the context of smart cities;
* \"Always-on\" connectivity provided by 5G supports services in high-mobility/vehicular environments, such as high-speed cars and trains, with strict requirements in terms of Doppler spread and multipath fading. One hundred percent geographical coverage is also required to support an intelligent traffic monitoring and management system. Many smart healthcare services also require an \"Always ON\" mode, as the reliability of communications is paramount when biomedically sensitive information is involved;
* Energy efficiency: The deployment of a massive number of antennas and devices can burden the energy requirements of a smart city ecosystem. Designation based on 5G ensures that smaller cells and reliable channel estimation leads to a minimum power consumption for all involved devices.  
Table 1 provides selected examples of services upon various domains of the smart world that 5G-enabled smart cities can support [21; 22; 23].
# Secure Audio-Visual Data Exchange for Android In-Vehicle Ecosystems
## 2 Setup and Methodology
### Experimental Devices and Methodology for Audio Key Extraction  
In the experiments we used several smartphones with different versions of Android, starting from older devices like the Nexus 7 to newer mid-range devices such as the Motorola E6 Plus. This allowed us to determine whether differences between Android APIs and device characteristics can create problems during the acoustic domain pairing. Also, as representative for our main use case, i.e., the in-vehicle scenario, we used an off-the-shelf PX5 Android infotainment unit. A summary of the used devices and some of their specifications are available in Table 2. On the left side of Figure 2 we depict the smartphones used in our experiments and in the right side of the figure we depict the devices placed on the vehicle dashboard.  
To depict the audio acquisition capabilities of the devices, on the left side of Figure 3 we show the power spectrum from the recordings by each device during a linear sweep signal between 20 Hz and 22 kHz that is played by a high-end HiFi system. In this figure, it can be seen that the frequency response of the microphone of the Nexus smartphone is more distinct than for the rest of the devices. On the right side of Figure 3, we depict the power spectrum of a linear sweep signal between 20 Hz and 22 kHz when played by the devices from our experiments and recorded by a calibrated UMIK-1 microphone. In this figure the frequency response of the speaker of the Nexus and Samsung A3 smartphone is  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Device Name** & **Release Year** & \
\begin{tabular}{c} **Android** \\ **Version** \\ \end{tabular} &
\begin{tabular}{c} **No. of** \\ **Microphones** \\ \end{tabular} \\ \hline
1. & Google Nexus 7 & 2012 & Android 5 & 2 \\
2. & Samsung Galaxy A3 & 2014 & Android 5 & 2 \\
3. & Samsung Galaxy S7 Edge & 2016 & Android 6 & 1 \\
4. & Motorola E6 plus & 2019 & Android 9 & 1 \\
5. & PX5 head unit & 2017 & Android 9 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Devices from our experiments.  
Figure 2: The four phones used in our experiments in-doors (**left**) and on the vehicle dashboard with the head unit (**right**).  
more distinct than the Samsung S7 and Motorola E6 speaker frequency response. For these measurements the distance between the speaker and the microphone was 100 cm.  
The distinct frequency responses from the speakers and microphones of our devices explain some of the disparities from the experiments that we present later. For example, for the experiments performed indoors, at 50 cm between devices, when the Nexus was used as emitter and the Motorola E6 as receiver, 100% of the keys were correctly received. However, when the emitter and receiver roles are shifted, i.e., Motorola E6 is the emitter and Nexus the receiver, only 70% of the keys are correct.
## 3 Midas GTS Simulation Process and Results
### Numerical Simulation Insights  
#### 3.5.1 Vertical Displacement  
As shown in Figure 6, (1) the excavation of the foundation pit had little influence on the vertical displacement of the existing operating tunnel. The arch of the existing up-line tunnel shows a settlement trend near the side of the foundation pit, with a maximum settlement of \(-0.04\) mm, which corresponds to the middle of the parallel plane between the foundation pit and the existing tunnel line. The arch waist of the tunnel shifts from the side of the foundation pit, where the maximum uplift value is \(0.06\) mm. The position corresponds to the corner of the parallel plane between the foundation pit and the existing tunnel line, particularly since the segment has a slight tendency to twist toward the direction of the foundation pit. (2) The over-crossing tunnels cause the progressive trend of the existing tunnel: After the shield tunneling of the new right-line tunnel, the existing tunnel corresponds to the arch crossing point of the new tunnel, and the existing tunnel is the largest, measured at \(1.49\) mm. (3) Following the shield tunneling of the new left-line tunnel, the floating of the existing tunnel increases continuously, and the maximum floating value is \(2.44\) mm, which corresponds to the middle part of the intersection of the right and left lines of the new tunnel and the existing tunnel plane. (4) The impact degree of the up-line near the foundation pit of the existing tunnel is greater than the down-line away from the foundation pit, and the maximum vertical displacement occurs in the up-line of the existing tunnel.  
Figure 5: Construction process of the finite element software simulation.
# All about Delay-Tolerant Networking (DTN) Contributions to Future Internet
## 3 Related Work
### Space
#### 3.1.2 Satellites
* _Maritime and Agriculture_: Enable communication among devices/sensors in the field and drones/satellites. They allow for remote monitoring and the management of operations for (i) satellite-terrestrial communication networks at sea [50; 51; 52]; (ii) gathering data, e.g., meteorological, moisture levels, temperature, humidity, crop health [53]; and (iii) precision agriculture [54; 55] and Machine Learning (ML) techniques [56]. * _Military_: Enhance surveillance and aerial reconnaissance operations [57; 58]. They provide reliable, global, and secure communication and navigation services while also gathering real-time intelligence by utilizing mainly LEO nano- and microsatellites.
# A Review of Digital Twin Technology for Electric and Autonomous Vehicles
## 2 Digital Twin in Transportation Systems
### Digital Twin in Electromobility  
Many applications of electromobility have been involved in research activities, leading to the phenomenon known as smart electromobility. The rapid growth of smart control systems has led to several developments in this industry. The data generated through this growth are the key factors in improving the smart mobility sector. The DT's strength lies in collecting and visualizing data and conducting statistics in which advanced analysis tools are used to improve manufacturing processes and help in decision-making.  
To charge an EV, it is commonly vital to physically attach/connect the EV plug to a charger located in a household or a public place through a charging cable. As EVs and self-driving cars become more prevalent and automated driving becomes more common, physical charging may become unmanageable, and automatic charging systems should be implemented. Generally, there are two options for automatic charging: parking the vehicle in an accurate position so that the vehicle's charging connector automatically fits the charging cable of an available charger or using wireless charging. [PERSON] et al. [20] introduced a DT vehicle simulation technique, focusing on two factors: (1) power consumption and (2) ride comfort. The simulated environment contains a vehicle model for interpreting the physical performance of the vehicle. An electronic control unit (ECU) has also been simulated as a prototype for regulating the simulated environment. They also developed an automatic charging system for EVs to charge the vehicle automatically after parking in an accurate position.  
BMS in electromobility is also essential concerning battery life, safety, and reliability. It relies on different types of sensors and actuators on the EV to provide real-time battery performance. Using an IoT platform to build a DT for BMS in the cloud boosts the robustness of the BMS. [PERSON] et al. [21] reviewed the solutions for BMS issues based on DT, such as the problems related to real-time estimation, dynamic charging control, and dynamic equalization control in a smart BMS. Another important application of the electric vehicle is the ADAS, built to enhance the driver's experience and passenger and pedestrian safety by decreasing vehicle accidents and alerting drivers of possible dangers. [PERSON] et al. [22] introduced a new vision-cloud data fusion approach to enhance the performances of visual guidance systems by leveraging DT technology and cloud servers. This work is one of the most effective studies to visualize the DT cloud data and support the ADAS or the drivers' decision-making.
## 4 Methodology
### Model Evaluation  
Several metrics were used to evaluate the performance of the developed machine-learning models. All of these metrics were derived from the confusion matrix, a two-dimensional contingency table that presents the relation between the actual and predicted classes [79]. A confusion matrix is comprised of four possible scenarios: true positive (TP), true negative (TN), false positive (FP), and false negative (FN). TP represents the number of incidents correctly classified within their respective duration categories (short, medium, intermediate, and long). TN represents the number of incidents that do not lie within the specific duration range and are correctly classified by the models as not being present within that range. FP represents the number of incidents that do not lie within the particular duration range but which are incorrectly classified by the models as present within that range, and FN represents the number of incidents that do lie within the specified duration range but which are incorrectly classified as not present within that range.  
In this study, four performance evaluation metrics--accuracy, recall/sensitivity, precision, and F1 score--were used. The mathematical formulas for each metric are shown in Equations (7)-(10). In addition, the macro average is introduced as a comprehensive evaluation metric for model performance. The macro average is calculated by averaging the metrics in each category.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{7}\]  
\[Recall=\frac{TP}{TP+FN} \tag{8}\]  
\[Precision=\frac{TP}{TP+FP} \tag{9}\]  
\[F1-\text{score}=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{10}\]
## 3 Experimental Results and Analysis
### Detection of Atmospheric Turbulence
#### 3.2.1 Kolmogorov Turbulence Detection
We analyzed the cause of this situation and found that, due to the existence of the aperture-smoothing effect, the area of the aperture leads to the generation of uncorrelated regions of light intensity fluctuation, and the total scintillation will be weakened due to the cancellation of uncorrelated elements, finally resulting in a small scintillation index. From Figure 9, the smaller the iris diaphragm diameter \(d_{0}\), the greater the Kolmogorov turbulence intensity obtained by inversion. In the experimental observation, the atmospheric turbulence intensity profile under different weather conditions was also detected. On 21 August 2022, the weather changed from overcast to light rain, with thick clouds in the sky and low temperatures. The diameter of the iris diaphragm is \(d_{0}\) = 0.8 mm, and the variation in the scintillation index detected with height is shown in Figure 10.  
The Kolmogorov turbulence intensity \(C_{n}^{2}\) profiles corresponding to the cloudy day on August 21 and the sunny day on September 3 were compared, as shown in Figure 11.  
Figure 11: Kolmogorov turbulence intensity profiles under different weather conditions. \[\beta_{I}^{2}=\frac{\sigma_{U}^{2}}{\left\langle{U}\right\rangle^{2}}=\frac{ \left\langle{U^{2}}\right\rangle-\left\langle{U}\right\rangle^{2}}{\left\langle {U}\right\rangle^{2}} \tag{6}\]0 Variation in scintillation index with height on cloudy days. It is clear that the scintillation index and turbulence intensity under cloudy conditions are significantly lower than those under sunny conditions, and the Kolmogorov turbulence intensity on cloudy days is approximately one order of magnitude smaller than that on sunny days. This is because the random fluctuation in atmospheric temperature is the main cause of the random fluctuation in the atmospheric refractive index, the temperature fluctuation on cloudy days is lower than that on sunny days, and the temperature fluctuation is less obvious than that on sunny days. On sunny days, solar radiation keeps heating the surface during the daytime, and the heat keeps transmitting upward, resulting in strong temperature fluctuations and a significant turbulence effect.
# Estimation of Litho-Fluid Facies Distribution from Zero-Offset Acoustic and Shear Impedances
## 4 Methodology  
The study workflow, as shown in Figure 2, begins with inverting the angle gathers into \(Z_{P}\), \(Z_{S}\), and density volumes and inverting the angle stacks into \(Z_{P}\) and \(Z_{S}\) volumes at the near, mid, and far incident angles. Next, the partially inverted impedances are used to forecast the zero-offset impedances by the statistical and MLFN models. The impedance volumes are then compared to each other by using the impedance logs. The best \(Z_{P}\) and \(Z_{S}\) volumes are applied to [PERSON]'s anisotropy equations to solve for the anisotropy parameters, Epsilon and Delta. The next step is to transform the impedance and density volumes into the elastic volumes, from which the sand and gas probabilities are estimated by using the logistic regression model. These probabilities are then inserted into a decision tree model to forecast the distribution of the facies classes: gas sand, wet sand, and shale. The inversion processes in our study were carried out by using the Hampson-Russel software provided by the CGG company.  
This study is not applicable to poststack data. The impedance model's accuracy depends on the seismic data quality as well as the angle gathers' range. The wider the angle range, the more enhanced signal-to-noise ratio and hence the more accurate the model. Moreover, both the P- and S-impedances should be available in order to solve for the anisotropy parameters: Epsilon and Delta. The study assumes a VTI medium which neglects some anisotropy-related factors such as the layers' dip angles and fractures; however, impedance modeling by MLFNs accurately solves for the zero-offset impedances by normalizing the error and mitigating the anisotropy effect simultaneously. The study's facies model is only applicable to the three facies classes: gas sand, wet sand, and shale. However, the model can be extended to include more classes. Moreover, the applicability of the model to other fields can be enhanced if there are more facies data available from a large number of wells.  
Figure 2: A flow diagram showing the methodology of estimating the facies distribution from the zero-offset acoustic and shear impedances, which are obtained from isotropic inversion, statistical modeling, and MLFN.
# Optimal Particle Filter Weight for Bayesian Direct Position Estimation in a GNSS Receiver
## 8 Simulations and Real-World Results
### Impact of Short, Medium and Far Multipath  
The impact on short, medium and far multipath on the probability function is shown on a strong multipath case \(\alpha_{MP}=0.9\). Basically, it is expected that such a strong multipath seldomly occurs in typical real-world scenarios, but it was chosen to clearly visualize the impact of an increasing multipath offset. It can be seen in Figure 10 that an increase in \(\Delta\tau_{MP}\) directly increases \(\sigma_{w}\).  
An interesting effect is the short constructive multipath case with an \(\Delta\tau_{MP}=3\) m. In this case, the weighted standard deviation \(\sigma_{w}\) is underestimated and shows a smaller variance than the LOS signal. This can be dangerous because this leads to a biased and at the same time more accurate measurement, compared to the truth. Considering the present multipath and incorporating it into the models for the weighting may prevent such an underestimation.  
Figure 8: Impact of different code delay bias standard deviations \(\sigma_{\Delta\tau}\) on the weights \(w_{k}^{i}\) for a coherent integration time \(T_{coh}=100\) ms. It can be seen that \(\sigma_{w}\) now approaches the theoretical limits for all \(\sigma_{\Delta\tau}\). Note also the significantly changed amplitude on \(|P|\).