esa-sceva/satcom-chunk-collection huggingface dataset:
# GPS Multipath Analysis Using Fresnel Zones
## 2 Multipath Theory
### Determination of the Attenuation Factor \(\alpha\)  
The attenuation factor \(\alpha\) represents the ratio between the amplitudes of the reflected signal \(A_{M}\) and the direct signal \(A_{D}\) and can therefore also be denoted as a relative amplitude. Following Equations (3) and (5), \(\alpha\) needs to be known for the determination of the phase error \(\Phi_{C}\) and the amplitude of the compound signal \(A_{C}\), respectively. Since both \(A_{M}\) and \(A_{D}\) cannot directly be measured by the receiver, instead, the provided signal-to-noise ratio (SNR) can be used to determine \(A_{M}\) and \(A_{D}\). Separating the direct and reflected signal strength components enables the determination of the relative amplitude.  
The gain patterns of GNSS antennas are usually designed in such a way that signals received at low or negative elevation angles are attenuated. Hence, for ground reflected signals, it can be assumed that the amplitude \(A_{M}\) is significantly lower than \(A_{D}\). Thus, the dominant trend in SNR time series refers to the direct signal, and the multipath signals are modulated on top of this trend.  
In Figure 4, an example for an SNR time series is shown.  
The amplitude of the direct signal \(A_{D}\) is modeled by fitting a polynom of lower order to the SNR time series, leading to the black curve shown in Figure 4. After subtracting \(A_{D}\) from the SNR time series, the amplitude of the reflected signal \(A_{M}\), especially at lower elevation angles, becomes more apparent. Finally, the epochwise attenuation factor \(\alpha\left(t\right)\) can be determined by  
\[\alpha\left(t\right)=\frac{A_{M}}{A_{D}}=\frac{\delta SNR}{A_{D}}. \tag{10}\]  
Alternatively, \(\alpha\) can also be determined from the antenna gain pattern and the reflection coefficients of the reflecting material. Since the gain pattern is not available for the antennas used in the investigations presented here, this approach is not explained in detail. For further information, refer to [36].  
Figure 3: Geometrical satellite-reflector-antenna configuration for the special case of a single horizontal reflector.
## 5 Conclusions  
In summary, we theoretically and experimentally compared the QEPAS performance based on circular and elliptical laser spots. According to the finite element (FEM) analysis software COMSOL, the sound pressure distribution on the QTF prongs from different laser spot was simulated and the experimental results are in excellent agreement with the simulation. In the case of normalized power, the QEPAS signal of the elliptical laser beam was 1.4 times higher than that of the circular beam, which proved that the elliptical spot can interact with more target gas molecules and make the inner surface areas of the QTF prongs fully utilized. The MDL and NNEA of 418.6 ppmv and \(1.51\times 10^{-6}\) cm\({}^{-1}\) W/\(\surd\)Hz were achieved, respectively. Since higher laser power can further enhance the performance of QEPAS sensors, this work provides an applied prerequisite for the combination of QEPAS system and high-power elliptical laser sources without complex shaping systems. In addition,  
Figure 8: Allan–Werle deviation plot of the QEPAS based sensor system at different averaging time.  
this technique would be more suited to low-cost, alignment-free QEPAS sensors without microtubes, which are a pain to align, especially in industrial devices.  
[PERSON] and [PERSON] performed the experiments and write the manuscript; [PERSON] and [PERSON] reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research is supported by National Natural Science Foundation of China (NSFC) (61805132, 61622503, 61575113, 11434007), Applied basic research program of Shanxi province (201801D221174); Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi(2019L0028); National Key R&D Program of China (2017 YFA0304203), Changjiang Scholars and Innovative Research Team in University of Ministry of Education of China (IRT_17R70), 111 project (D18001), Outstanding Innovative Teams of Higher Learning Institutions of Shanxi, Foundation for Selected Young Scientists Studying Abroad, Sanjin Scholar (2017 QNSJXZ-04) and Shanxi \"1331 KSC\".  
We thank [PERSON] and [PERSON] for valuable discussions.  
The authors declare no conflict of interest.
# Feasibility Analysis of Online Uncertainty Metrics for PMU-Based State Estimation
## 1 Introduction
#### Paper Contributions  
In this context, the present paper investigates the feasibility of using the reliability metric in PMU-based state estimation applications. By extending the analysis presented in [24], we assess the confidence interval of the selected metric as a function of their mathematical definition and the considered operating conditions. This way, we identify which uncertainty contributions are suitably quantified and which are neglected or misrepresented.  
To the state-of-the-art, the reliability metrics have been employed for a posteriori analysis of unexpected estimate distribution or contingencies. In this paper, we propose an online application where each metric is computed locally and then employed globally to minimize the impact of definitional uncertainty effects.  
In more detail, we consider two well-known state estimation approaches, namely Weighted Least Squares (WLS) and Discrete Kalman Filter (DKF) [25; 26]. The choice of these two approaches is not only based on their popularity and wide employment in many state estimation approaches. Indeed, WLS and DKF allow us to account for the measurement uncertainty directly in the problem statement. Instead of considering predefined and constant criteria (e.g., the instrument transformer class), the uncertainty associated with each measurement is assessed online and directly included in the state estimation routine.  
We do not consider purely standard conditions for this analysis, but we reproduce a more realistic operating scenario. In the MATLAB R2022b Simulink environment, we simulate the well-known IEEE 14-bus system [27], and populate it with PMU models compliant with the IEC Std requirements [28]. By substituting the original load with non linear time-varying models, we can reproduce dynamic conditions compatible with high shares of renewable energy sources. Throughout extensive numerical simulations, we highlight the proposed approach's performance enhancement and theoretical limits. The statistical distribution of the state estimation errors confirms how the most limiting factor is represented by the improper assumption of constant PMU reliability, independently from the current operating condition. We also validated this consideration by applying a different model of the instrument transformer contributions.  
The paper is organized as follows. Section 2 presents the main uncertainty contributions inherent in any PMU-based measurement chain. In Section 3, we introduce the online performance metric to assess modeling inconsistencies between measurand and reference signal model and characterize its statistical distribution in operating conditions inspired by real-world data sets. Section 4 describes the configuration of the simulation model and discusses the most suitable setting of its main parameter values. In Section 5, we present the state estimation results as a function of the selected measurement weighing criteria. Finally, Section 6 provides some closing remarks.
# Trajectory Optimization with Dynamic Drivable Corridor-Based Collision Avoidance
## 1 Introduction  
Autonomous driving technology plays a crucial role in improving driving safety, optimizing traffic flow, and enhancing the overall driving experience [1, 2, 3]. Trajectory planning, a critical component within autonomous driving systems, aims to generate safe and feasible trajectories that adhere to vehicle dynamics constraints and effectively address collision avoidance in complex environments [4, 5, 6]. Accurate trajectory planning enables autonomous vehicles to navigate smoothly and safely through dynamic scenarios, offering users an intelligent and comfortable driving experience. Collision avoidance, which ensures that autonomous vehicles can safely navigate around obstacles without collisions, is a core issue in trajectory planning. This directly determines the safety and reliability of autonomous driving systems [7]. This challenge is particularly pronounced in unstructured environments lacking clearly defined road boundaries or standardized traffic rules, characterized by irregular obstacle distributions and complex terrains. Recent studies have investigated diverse approaches to address these challenges, with optimization methods based on dynamic drivable corridors (DCs) receiving increasing attention.
# An Adaptive Throughput-First Packet Scheduling Algorithm for DPDK-Based Packet Processing Systems
## 4 Results
### Packet Distribution  
The packet distribution is a direct fiction of the load balancing performance of those hash-based packet scheduling algorithms. In our tests, the length of packets generated by Ixia are almost the same; therefore, we show the number of packets on each processing core (Worker) under different network loads to describe the load balancing performance of different scheduling algorithms, as shown in Figure 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Threshold (\%)** & **Probability of Packet Drop (\%)** \\ \hline
75 & 73.4 \\ \hline
50 & 55.4 \\ \hline
25 & 42.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The probability of packet loss rate under different thresholds.  
The Ideal curve represents the theoretical optimal allocation; in other words, the number of packets on each processing core is \(\frac{pkt\_num}{core\_num}\), where \(core\_num\) is the number of processing cores. In our test, \(core\_num\) is 4. Intuitively, RR would achieve a similar distribution with Ideal; however, to be consistent with our Subflow-based packet scheduling, RR is performed on each burst, not for the whole traffic. Since the number of packets fetched from the NIC in the burst varies with each retrieval, the load distribution of RR is as Figure 2 shows.  
As shown in Figure 2, the number of packets on each core is a little unbalanced for HRW and the proposed method, compared with RR and CRC32. As mentioned above, the primary purpose of our proposed method is to achieve higher throughput and lower packet loss rates. Only when the load imbalance leads to packet loss will the scheduling decision be adjusted. As we can see, CRC32 achieves an excellent load distribution, approaching the Ideal distribution, which shows that it is not a bad idea to use it as the scheduling algorithm for a packet processing system.
# Estimation of River Velocity and Discharge Based on Video Images and Deep Learning
## 2 Theory and Methods
### Camera Calibration  
After the texture angle of the space-time image is obtained, it is necessary to further utilize the principle of projection transformation to achieve the conversion between the world coordinate system and the pixel coordinate system, so as to obtain the actual distance represented by each pixel, which is denoted by \(k_{x}\). Having selected a calibration point on the riverbank as the origin of the world coordinate system, the line connecting this origin to another calibration point on the same horizontal plane is considered the x-axis direction,  
Figure 6: BAM attention module.  
and the perpendicular line from the origin to the calibration point on the opposite bank is considered the y-axis direction. According to the positional relationship of the ground calibration points, the world coordinates of each calibration point are measured by a total station, and the pixel coordinates correspond one by one. Assuming a point \((i,j)\) in the pixel coordinate system corresponds to the position \((x,y,z)\) in the world coordinate system, the transformation relationship can be expressed as Equation (6), \(\eta_{mn}(m,n=1-3)\) is the corresponding transmission transformation matrix.  
\[\begin{bmatrix}x\\ y\\ z\end{bmatrix}=\begin{bmatrix}\eta_{11}&\eta_{12}&\eta_{13}\\ \eta_{21}&\eta_{22}&\eta_{23}\\ \eta_{31}&\eta_{32}&\eta_{33}\end{bmatrix}\begin{bmatrix}i\\ j\\ 1\end{bmatrix} \tag{6}\]  
After expanding the equation, the final coordinate relationship is shown in Equation (7). Therefore, the position of the ground calibration points and the velocimetry points on the river section in the world coordinate system can be accurately marked in the pixel coordinate system.  
\[\begin{cases}x=\frac{\eta_{11}i+\eta_{12}i+\eta_{13}}{\eta_{31}i+\eta_{32}i+ \eta_{33}}\\ y=\frac{\eta_{21}i+\eta_{22}i+\eta_{23}}{\eta_{31}i+\eta_{32}i+\eta_{33}}\end{cases} \tag{7}\]
## 2 Theory
### Ray Theory  
In this study, ray theory is used to calculate the sound field distribution of directional point sound sources in the ocean channel. The ray theory calculates the target sound field in the waveguide, as shown in Figure 1c. The sound pressure of the sound field can be expressed in the following form [21]:  
\[p(x,y,z,t)=A(x,y,z)e^{\beta[\omega t-k_{0}\varphi(x,y,z)]}, \tag{9}\]  
where \(A\) is the sound pressure amplitude, which is, the sound pressure amplitude of the directional point source obtained in Section 2.1; the wave number is expressed as \(k=\frac{\omega}{c_{0}}\cdot\frac{c_{0}}{c}=k_{0}n(x,y,z)\), where \(c_{0}\) is the speed of sound at the reference point and \(n(x,y,z)\) is the refractive index; \(k_{0}\varphi\) is the phase; and, \(\varphi(x,y,z)=n(x,y,z)\cdot\varphi_{1}(x,y,z)\) is the eikonal, which is the length dimension that indicates the change of phase. By substituting this form of solution into the wave equation, the following relational expression can be obtained:  
\[\frac{\
abla^{2}A}{A}-{k_{0}}^{2}\
abla\varphi\cdot\
abla\varphi+k^{2}=0, \tag{10}\]  
\[\
abla^{2}\varphi+\frac{2}{A}\
abla A\cdot\
abla\varphi=0. \tag{11}\]  
From the eikonal equation and [PERSON]'s law, we can obtain \(\varphi(x,y,z)\). According to [PERSON] theorem, the sound ray trajectory equation, and [PERSON]'s law, and while ignoring the constant factor, the sound pressure amplitude is reflected as the root value of the sound intensity. The trajectory of the sound rays that are emitted at any angle from the sound source in the seawater channel can be solved.
## 2 Methodology
### Computational Domain and Boundary Conditions  
The overset mesh technology is employed here to reproduce the real motion of a train, which has been extensively used to examine trains traveling through tunnels or tubes [40; 41; 42]. The computational domain is divided into an overset region (moving region) close to the train and a background region (stationary region) including the tunnel and the area of the far field. The layout of the computational domain and the boundary conditions are illustrated in Figure 4. The schematic diagrams are not scaled to provide a better representation of the layout of the computational domain.  
As this research primarily focuses on the initial compression wave characteristics induced by the train entering the tunnel, the computational domain does not include the far-field region of the tunnel exit [43; 44], thus leading to a reduction in grid count and computation costs. To prevent pressure wave reflection, the far-field region and the tunnel exit are set with free-stream boundaries and non-reflective Riemann boundary conditions [45; 46]. The dimensions of the computational domain are 300 m in the streamwise direction and 200 m in the vertical direction. At the initial moment, the high-speed train is positioned 280 m away from the far-field region on the left side. To avoid the numerical generation of compression waves, the train is gradually accelerated from a stationary state to 400 km/h using a fifth-order polynomial to describe the velocity profile [30], with an acceleration distance of 140 m. The expression for the velocity profile is as follows:  
\[V_{tr}(t)=\frac{a}{20}t^{5}-\frac{at_{1}}{8}t^{4}+\frac{at_{1}^{2}}{12}t^{3}, \tag{1}\]  
Figure 4: Schematic diagrams of the computational domain and boundary conditions at \(t=0\) s: (**a**) side-view; (**b**) top-view (not to scale).  
Figure 3: The distribution of the cross-sectional area and change rate of the streamlined train nose along the flow direction.  
where \(a=120V_{tr}/t_{1}^{5}\), \(t_{1}\) represents the acceleration time of the train and \(V_{tr}\) denotes the speed at which the train enters the tunnel. This velocity expression ensures that the acceleration of the train is zero at both the beginning and the end of acceleration. At a distance of 100 m from the tunnel entrance, the train completes the acceleration process and then enters the tunnel at a constant speed of 400 km/h. The tunnel length is 500 m, and based on preliminary calculations and subsequent results, this length has been confirmed to ensure the generation of a complete initial compression wave. The far-field pressure is at a standard atmospheric pressure of 101,325 Pa, with a temperature of 288 K.
## 3 Experiment and Analysis  
In this section, the AMG cycle slips detection method is evaluated though a field test by collecting BDS observations in the severe multipath environment and weak multipath environment. First, the experimental design is described in detail. Second, the influence of the multipath environment on the cycle slips' detection and cycle slips' completeness is tested and analyzed. In the end, several different groups of cycle slips are added artificially to validate our points.
# AUV Adaptive Sampling Methods: A Review
## 3 Target of Interest
### Chemical Features
#### Oil Plume  
Identification of an oil spill can be made via either direct detection of hydrocarbons or in an indirect manner by identifying the changes of baseline properties. The available sensors and detection methods for in-situ measurements [63] were recently summarised as given in Table 8. Non-dispersive infrared (NDIR) spectrometry measures the number of methane (CH\({}_{4}\)) molecules by infrared absorption signals, when dissolved CH\({}_{4}\) molecules are diffused into the detector chamber through a membrane. A Fluorometer measures the fluorescence parameter from the intensity and wavelength distribution of the emission spectrum after excitation by a certain spectrum of light. Oils typically absorb light in the ultraviolet spectrum (300-400 nm) and emit light in the visible spectrum (450-640 nm range) [64]. The number of specific molecules in a medium can be known by the measured fluorescence parameters. A chromophoric dissolved organic matter fluorometer (CF) is one of the most widely used sensors in practice. CF can directly measure the concentration of hydrocarbons. Particle size analysers can determine the size distribution in a sample using static light scattering instruments. Mass spectrometers generate a mass spectrum which represents the atomic mass units (amu) of sample components. Based on quantified ionised molecules, this method can provide detailed information on the oil and water mixtures including the fingerprint of the oil. Generally, multiple sensors are used to achieve comprehensive understanding of an oil spill [65] and to take into consideration false positives. Therefore, a combination of direct and indirect sensors are typically used in practice. Qualitative assessments in terms of their performance, strengths, limitations and working principle of each in-situ method have been investigated by [PERSON] [66]. In general, oil is more buoyant than sea water. In the case of crude oils, a third of the volume of a slick evaporates within the first day. However, a non-buoyant oil plume has also been reported [67] during the largest anthropogenic oil spill accident following the Deepwater Horizon offshore drilling rig explosion in 2010 in the Gulf of Mexico. This was an unprecedented case, as approximately 780,000 m\({}^{3}\) of crude oil was spilled and a significant amount of this oil was trapped in the deep-water column at around a depth of 1000 m. [PERSON] et al. [68] developed a semi-autonomous method using data-denial simulation for data interpretation and adaptive sampling plan design. They integrated a human interaction with modern machine learning technique for data pre-processing. Three data sources, including chemical tracer data (aromatic hydrocarbon) and methane concentration (ion peaks) from the TETHYS mass spectrometer from Sentry AUV and real-time water current profiles from ADCPs,
# On the Performance of a UAV-Aided Wireless Network Based on NB-IoT
## 6 Conclusions  
In summary, we proposed a thorough network performance evaluation of an UAB-aided NB-IoT network through detailed simulations. We considered the different aspects of the NB-IoT protocol, including the signalling granting resources for uplink transmission and the NB-IoT coverage classes. Then, we jointly evaluated the system performance on the service offered, access rate, average latency, network throughput and energy consumption metrics. UAB speed and height reveal a noticeable impact on the final performance, requiring a performance trade-off on the different metrics. Finally, we also observed the implications of different trajectory selection. A trajectory given by the TSP solution is the most suitable for clustered environments. The presented approach applies to IoT applications not constrained in time and does not consider the UAV battery expiration before the end of the trajectory. Indeed, this would modify all the trajectories to include the time dimension meeting the application requirements and an energy threshold before which the UAV needs to recharge its batteries. This study is left for further works, together with the introduction of multiple UAB service.  
Conceptualization, C.B. and S.M.; methodology, C.B. and R.M.; software, R.M. and S.M.; validation, S.M., R.M. and C.B.; investigation, S.M.; data curation, S.M.; writing--original draft preparation, S.M. and R.M.; visualization and supervision, C.B. and R.V.; funding acquisition, R.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.