esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Fitting Cubic Normal Distribution of Epistemic Uncertainties
### Cubic Normal Distribution  
After determining the first four statistical moments \(M_{1}\sim M_{4}\) of epistemic uncertainties \(x\), the standardized variable \(x_{s}\) of \(x\) is transferred to a cubic function of a random variable \(u\) using Equation (18):  
\[\frac{x-\mu_{x}}{\sigma_{x}}=x_{s}=S_{u}(u)=a_{4}u^{3}+a_{3}u^{2}+a_{2}u+a_{1}, \tag{18}\]  
where \(\mu_{x}=M_{1}\) and \(\sigma_{x}=\sqrt{M_{2}}\) are the mean and standard deviation of \(x\), respectively. \(a_{1}\sim a_{4}\) are the functions of four statistical moments \(M_{1}\sim M_{4}\); the calculation details can be found in [39].  
After determining coefficients \(a_{1}\sim a_{4}\), the complete monotonic expression of \(u\) is decomposed into six types according to the values of \(q=\frac{3a_{2}a_{4}-a_{3}^{2}}{3a_{4}^{2}}\), \(a_{4}\), and \(M_{3}\), which are summarized in Table 1. The six types include unbounded distributions (Types I and VI), unilaterally bounded distributions (Types II, III, and V), and a bilaterally bounded distribution (Type IV). The definitions of parameters \(A\), \(B\), \(\Theta\), \(r\), \(\theta\), \(J_{1}^{*}\), and \(J_{2}^{*}\) are illustrated in [39; 40].  
The PDF \(p(x)\) of epistemic uncertainty \(x\) is calculated based on the PDF \(\phi(u)\) of \(u\) using Equation (19):  
\[p(x)=\frac{\phi(u)}{\sigma_{x}(3a_{4}u^{2}+2a_{3}u+a_{2})}. \tag{19}\]  
When the four statistical moments of epistemic uncertainty \(x\) are determined, the transformational continuous PDF can be calculated using the above methods. The calculated PDF and cumulative density function (CDF) of the standardized variable \(x_{s}\) (mean \(M_{1}=0\) and variance \(M_{2}=1\)) with different levels of skewness \(M_{3}\) and kurtosis \(M_{4}\) are shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Parameters** & \(\mathbf{u}\) & **Range of \(\mathbf{x}\)** & **Type** \\ \hline \(q\geq 0\) & & & \(\sqrt[3]{A}+\sqrt[3]{B}-\Theta/3\) & \((-\infty,+\infty)\) & IV \\  & & \(M_{3}\geq 0\) & \(2r\cos(\theta/3)-\Theta/3\) & \(\int_{1}^{*}<x<J_{2}^{*}\) & II \\  & \(a_{4}>0\) & & \(\sqrt[3]{A}+\sqrt[3]{B}-\Theta/3\) & \(x\geq J_{2}^{*}\) & \\ \(q<0\) & & \(M_{3}<0\) & \(-2r\cos[(\theta-\pi)/3]-\Theta/3\) & \(J_{1}^{*}<x<J_{2}^{*}\) & III \\  & \(a_{4}<0\) & & \(-2r\cos[(\theta+\pi)/3]-\Theta/3\) & \(\int_{2}^{*}\leq x\leq J_{1}^{*}\) & I \\  & \(a_{4}=0\) & \(M_{3}\
eq 0\) & \(\frac{-a_{2}+\sqrt{a_{2}^{2}+4a_{3}(a_{3}+x_{s})}}{2a_{3}}\) & \(a_{2}^{2}+4a_{3}(a_{3}+x_{s})\geq 0\) & V \\  & & \(M_{3}=0\) & \(x_{s}\) & \((-\infty,+\infty)\) & VI \\ \hline \hline \end{tabular}
\end{table}
Table 1: Complete monotonic expression of \(u\).
# Retrieval of Nearshore Bathymetry around Ganquan Island from LiDAR Waveform and QuickBird Image
###### Abstract  
Optical remote sensing is an effective means of water depth measurement, but the current approach of mainstream bathymetric retrieval requires a large amount of onsite measurement data. Such data are hard to obtain from places where underwater terrains are complicated and unsteady, and from sea areas affected by issues with rights and conflicts of interest. In recent years, the emergence of airborne light detection and ranging (LiDAR) provided a new technical means for field bathymetric survey. In this study, water depth inversion was carried out around an island far from the mainland by using remote sensing images and real LiDAR waveform data. Multi-Gaussian function fitting was proposed to extract water depth data from waveform data, and bathymetric values were used as control and validation data of the active and passive combination of water depth inversion. Results show that the relative error was 5.6% for the bathymetric retrieval from LiDAR waveform data, and the accuracy meets the requirements of ocean bathymetry. The average relative error of water depth inversion based on active and passive remote sensing was less than 9%. The method used in this study can also reduce the use of LiDAR data and the cost, thus providing a new idea for future coastal engineering application and construction.  
optical remote sensing; LiDAR waveform; bathymetric retrieval; active and passive combination Article
# Comparison of Modern Deep Learning Models for Speaker Verification
## 5 Experiment Setup
These findings are detailed in Table 4.  
Further analysis of the results shows that the better-performing models, namely TitaNet and Ecapa, exhibit greater disparities between intra-class (same class) and inter-class (different class) distances compared to the less effective models, such as WavLM. Notably, the standard deviation for the different-class distances with the WavLM model is much higher than the other models. These observations explain why the TitaNet and Ecapa models performed so well and why the WavLM model did not. This variability implies that embeddings from WavLM are more dispersed, complicating the accurate differentiation between speakers. Figure 5 visualizes some of the speaker embeddings, offering a clearer perspective on the experimental findings. Figure 5: Speaker embeddings visualization for respective models: PyAnnote, WavLM, TitaNet and ECAPA. The axes represent the main PCA components for the voice sample embeddings of five exemplary Ukrainian politicians. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & \
\begin{tabular}{c} **The Distance between** \\ **Samples of Different Classes** \\ \end{tabular} &
\begin{tabular}{c} **The Distance between** \\ **Samples of the Same Class** \\ \end{tabular} \\ \hline PyAnnote & 1.298 \(\pm\) 0.071 & 0.941 \(\pm\) 0.117 \\ \hline WavLM-Base-SV & 0.654 \(\pm\) 0.238 & 0.286 \(\pm\) 0.075 \\ \hline WavLM-Base-Plus-SV & 0.684 \(\pm\) 0.254 & 0.286 \(\pm\) 0.073 \\ \hline TitaNet-Large & 1.246 \(\pm\) 0.084 & 0.773 \(\pm\) 0.121 \\ \hline Ecapa & 1.293 \(\pm\) 0.081 & 0.827 \(\pm\) 0.119 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Calculated speaker embedding parameters. The prior observations are further confirmed by the visualizations. It can be seen that the embeddings of the better-performing models (TitaNet, Ecapa) are more tightly clustered among the speakers, which facilitates more accurate speaker verification. In contrast, the WavLM model produces more scattered embeddings, resulting in less distinct groupings, and, consequently, less reliable verification.
## 4 Conclusions  
The buckling of a system, including a tensile rod with one end moving along a linear sliding guide and the second end connected to a circular sliding guide, was studied theoretically and experimentally for pinned, rigid, and semi-rigid connection schemes. A device for experimental investigation of the rod critical tensile load is proposed. The device can provide higher accuracy compared to known alternative devices. This is achieved by approaching the maximum load to the critical one. The achieved accuracy, estimated by the discrepancy between the calculated and the experimental values depending on the \(L\)/\(R\) ratio, was 2.1-3.5%.  
In the frame of the study, the following characteristics were determined:  
* dependency of natural vibration frequencies on tensile load, cross-sectional dimensions of the elastic rod, and mass on its end;
* values of the critical load for different connection schemes, considering elastic compliance in the attachment nodes;
* range of the ratio between the rod length \(L\) and the radius of the rod end trajectory \(R\) that yields a linear dependence between the transverse stiffness of the system and the tensile load, allowing the application of a dynamic method to obtain the critical load with minimal error.  
It was experimentally confirmed that:  
* an elastic rod with various types of end connection schemes buckles under a tensile load;
* the free vibrations feasibility is subjected to changing the tension load from 0 to a value that is close to the critical load;
* it is possible to use the linear approximation model to obtain the dependence of the squared natural vibration frequency \(f^{2}\) on the tensile load \(N\);
* the calculation model of the system, buckling conditions, and critical load value correspond to the physical model.  
The results of this study can be used to design mechanisms including elements with ends moving along curved sliding guides.  
Conceptualization, E.E. and B.B.; formal analysis, B.B. and E.E.; methodology, B.B., E.E. and Y.R.; investigation, B.B., E.E.; software, B.B. and E.E.; validation, E.E. and Y.R.; visualization, E.E.; writing--original draft, B.B., E.E; writing--review and editing, E.E. and Y.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.
# Path Loss Measurements and Model Analysis in an Indoor Corridor Environment at 28 GHz and 38 GHz
## 3 Path Loss Measurements and Models
### Measurement Setup
Figure 6: The setup of the transmitter and the receiver in the indoor corridor environment. The transmission of a continuous wave signal between the two broadband horn antennas used in the transmitting and receiving ends had antenna heights of 1.6 m at the transmitting antenna stand and 2.3 m at the receiver's end. Both polarizations were placed in the vertical and horizontal directions; the center frequencies used were 28 and 38 GHz. For the V-V polarization, the Tx and Rx broadband horn antennas were vertically polarized. On the other hand, the Tx antenna for the V-H was vertically polarized, while the Rx antenna was horizontally polarized. The transmitting antenna was placed at one end of the corridor, and the receiver antenna was moved away from the transmitter by 2 m until it reached a distance of 24 m. Meanwhile, specialists in this field proposed a reference distance of 1 m between the transmitter and receiver. With a reference distance do = 1 m, the number of Tx-Rx separation distances was 13. When both antennas were aligned on the bore sight with no obstacles in the transmitting signal path between them, the LOS scenario was evaluated. However, in the NLOS, the received power was determined when the transmitting antenna was rotated to make sure that there was an obstruction resulting in no clear optical path between the Tx and Rx antennas. At this point, there was no aligning bore sight between the two antennas. The floor layout and the inside corridor are depicted in detail in Figures 3 and 7, respectively. It is worth noting that during the campaign, measuring cautions were observed in order to acquire accurate measurements. During the campaign, all doors were closed and human movement along the inside corridor was prohibited. In addition, all objects in the corridor were eliminated. The path loss was calculated using Equation (1), having considered the transmitted power \(P_{t}\), received power \(P_{r}\), gain of the transmitting antenna \(G_{t}\) as well as the gain of the receiving antenna \(G_{r}\). The path loss equation (all parameters in dB) is as follows, as computed in [21]:  
\[P_{L}=P_{t}-P_{r}+G_{r}+G_{t} \tag{1}\]
## 3 Extremum Seeking with Prescribed Performance Control
### Extremum Seeking Control
The ESCWAO is designed to reduce the magnitude of the dither signal as the estimated gradient approaches zero. Figure 2: Single parameter basic ESC design with the perturbation signal. This approach can effectively reduce steady-state oscillations and improve the performance of the ESC for platoons. The specific design of the ESCWAO may vary depending on the particular application and the characteristics of the system being controlled. However, in general, the approach involves modifying the gain of the dither signal based on the estimated gradient. By adapting the magnitude of the dither signal to the estimated gradient, the ESCWAO can effectively reduce oscillations and improve the performance of the controller. Figure 3 shows that the ESCWAO design is similar to the standard ESC with the difference that there is not a constant amplitude \(a\) but an adaptive law is proposed for the decay in excitation signal amplitude. Moreover, the demodulated signal after high-pass filtering is multiplied without the amplitude. Dynamic analysis of the system is identical to standard ESC and the dithered signal is added to the system as indicated in Equation (14):  
\[d_{i}=-\lambda_{2_{i}}\,g_{2}(a_{i})\,\exp(-\gamma_{2_{i}}|\xi_{i}|),\quad a_ {i}(t_{0})=a_{i_{0}}>0 \tag{19}\]  
where, \(\lambda_{2_{i}},\gamma_{2_{i}},a_{i_{0}},\;\;for\;\;i=1,\ldots N\), are positive design parameters. For the stability proof of the proposed algorithm, the same assumptions as stated in standard ESC are followed, whereas for the extra dithered signal, further analysis can be found in [21]. ESCWAO requires parameters \(\omega,\;\omega_{l},\;\omega_{h},k,\;\lambda_{2},\;\gamma_{2},a(0),\;\hat{ \theta}(0)\) to be fine-tuned. Henceforth, the reduction in steady-state oscillations achieved by the ESCWAO leads to efficient design. However, it is important to note that proper tuning of the ESCWAO is necessary to achieve the desired performance, which sometimes is a labour-intensive process. More specifically, filter cut-off frequencies need to be lower than the frequency of the perturbation signal and the adaption gain \(k\) value should also be small to achieve the desired time scales of the system [8] and properly estimate the optimal value. Additionally, the \(\gamma_{2}\) parameter determines the rates of decay of the dither signal. Lowering the value will lead to bigger decays and the results will be similar to the standard ESC. Thus, at the beginning of the tuning process, the parameter should be sufficiently small in order to detect at which point the ESCWAO converges to the optimal point. Afterwards, the designer should increase the value and maintain it at a point that ensures faster decay. Figure 3: Single parameter ESCWAO design. The previously constant \(a\) signal is now replaced by the scheme of the dithered signal.
## 1 Introduction
Subsequently, targeted measures can be recommended to enhance efficiency and safety, addressing critical aspects in a country where the quality of infrastructure significantly impacts transportation safety and efficiency [7; 8; 9; 10; 11]. In tandem, the application of HMM for predictive modeling proves instrumental in transforming transportation dynamics. HMM predicts transportation costs and CO\({}_{2}\) emissions based on historical data, providing valuable insights for decision-making. Utilizing historical data for training, the HMM enhances prediction accuracy and aids in optimizing transportation planning [7; 8; 9; 10; 11]. This paper marks a paradigm shift in multimodal transportation decision-making by introducing a dynamic fusion of DEA and HMM. The study pioneers a holistic framework that simultaneously evaluates financial and environmental impacts, showcasing a novel approach in the cargo sector. The real-time adaptation to efficiency scores and predictive modeling further contributes to the unprecedented depth and breadth of this research. The primary contribution lies in emphasizing the crucial need for adopting multimodal transportation optimization in Iraq, utilizing advanced techniques, specifically DEA and HMM, as invaluable assets for addressing the complex challenges within the nation's transportation sector. These tailored approaches are recognized for their capacity to enhance infrastructure, increase transportation efficiency, reduce emissions, and ensure the safety and security of transportation routes. Moreover, the paper advocates for the integration of these advanced techniques as pivotal elements in Iraq's journey toward establishing a modern, efficient, and sustainable transportation network. It positions DEA and HMM as instrumental tools in unlocking Iraq's full economic potential through improved logistics and transportation systems. The acknowledgment of intricate environmental impacts associated with transportation and logistics systems is a notable aspect, with an emphasis on proactive environmental management strategies. The paper urges a transition from mere regulatory compliance to a forward-thinking approach, identifying negative interactions, impact types, and alternative methods to control pollution and natural resource degradation, aligning with contemporary environmental standards and sustainable practices [3; 4; 12; 13; 14]. The structure of this paper is as follows: Section 2 presents the literature review, offering a comprehensive overview of existing studies and findings related to multimodal transportation networks. Section 3 explains the system model and design methodology. Section 4 presents a performance evaluation of the paper, including simulation environmental and simulation results evaluating the performance of the multimodal transportation network system using various metrics. Finally, Section 5 concludes the paper by summarizing the key findings and contributions of the study.
## 4 Wireless Enabling Technologies
### MmWave Communications  
MmWave communication is a promising technology for also providing a very high data rate for mobile devices. MmWave works with wavelength on the order of millimeters and includes the frequency band [30 300] GHz [34].  
As for VLC, the propagation loss is much higher than in traditional radio frequency bands. Specifically, in this case, the free space propagation loss is proportional to the square of the carrier frequency. With a wavelength of about 5 mm, for example, the free space propagation loss at 60 GHz is 28 dB higher than at 2.4 GHz [35]. In addition, the oxygen absorption in the 60 GHz band has a peak, ranging from 15 to 30 dB/km. Hence, transmissions can easily be blocked by obstacles. This aspect implies on the one hand that signals could be limited in roads, but, on the other hand, the system has limited coverage and reliability.  
In spite the challenging issues related to the higher frequencies, mmWave communications are highly considered for future VSNs with the aim to provide a very high capacity.  
More than one international organization worked on its standardization, including the European computer manufacturers association (ECMA) [36], IEEE 802.15.3 Task Group 3c (TG3c) [37], IEEE 02.11 ad standardization task group [38] and consortia, such as the WirelessHD consortium, and the Wireless Gigabit Alliance (WiGig).  
As far as IEEE 802.11 ad is concerned, it specifies the PHY and MAC layer in the 60 GHz band. In the physical layer, two operating modes are defined: the first is based on OFDM for high performance applications and high data rate, the second is based on a single carrier (SC) for low power and low complexity implementation. At the MAC layer, a hybrid multiple access of CSMA/CA and time-division multiple access (TDMA) are adopted: CSMA/CA is more suitable for bursty traffic and reduced latency, while TDMA is more suitable to support high quality of service (QoS).  
MmWave radar is widely used in vehicles for applications such as adaptive cruise control and collision avoidance [39] or for opportunistic target detection and parameter estimation [40]. MmWave communications are also considered for future 5G systems. In fact, as it will be better detailed in Section 4.5, 5G new radio (NR) allows a cellular-based system to operate in a wide range of carrier frequencies from a sub-6 GHz band to an mmWave band with appropriate handling of a multi-path delay spread and phase noise depending on the carrier frequency.
## 2 Methods and Instruments
### Swarm Satellites  
The Swarm constellation was designed to provide an advanced survey of Earth's magnetic field. It consists of three identical satellites--Alpha, Bravo, and Charlie (A, B, and C)--launched on 22 November 2013 into near-polar orbits. The mission reached its final orbital configuration on 17 April 2014, positioning Swarm A and C as a lower pair, flying side by side at an initial altitude of 462 km, separated by approximately 1.4\({}^{\circ}\) in longitude (east-west) and about 50 km in latitude (north-south). These two satellites have an inclination angle of 87.35\({}^{\circ}\) and travel at roughly 7.5 km/s. Meanwhile, Swarm B cruises at a slightly higher altitude of 511 km and an inclination of 87.75\({}^{\circ}\), drifting slowly away from the Swarm A-C pair by about 24\({}^{\circ}\) per year in terms of the longitude of the ascending node. In October 2021, the orbital planes realigned, with Swarm B counter-rotating with respect to Swarm A and C. To maximize the scientific return of this conjunction, the Swarm A and C satellites were brought back into the same orbital plane, starting with a manoeuvre in October 2019 that would slowly reduce the separation in longitude, achieving a perfect alignment at the exact time of the conjunction with Swarm B. During the conjunction, the distance between the Swarm A and C satellites was reduced to only 2 s along the orbit (15 km), which was previously confined between 4 and 10 s \"([[https://earth.esa.int/eogateway/missions/swarm](https://earth.esa.int/eogateway/missions/swarm)]([https://earth.esa.int/eogateway/missions/swarm](https://earth.esa.int/eogateway/missions/swarm))) (accessed on 18 October 2024)\". The constellation enables multipoint measurements on both global and regional scales, offering more comprehensive spatial and temporal coverage than a single satellite can provide. Each Swarm satellite carries identical scientific instrumentation, including a Vector Field Magnetometer (VFM), an Absolute Scalar Magnetometer, and an LP. The LP collects in situ electron density and temperature data at a 2 Hz sampling rate [41], which is effective for detecting and characterizing topside ionospheric structures and irregularities [36].  
For this study, 2 Hz Ne data from Swarm A, B, and C were analyzed as a function of latitude and universal time (UT) to identify topside ionospheric irregularities. The data, accessible to users on the ESA website \"([[https://earth.esa.int/web/guest/swarm/data-access](https://earth.esa.int/web/guest/swarm/data-access)]([https://earth.esa.int/web/guest/swarm/data-access](https://earth.esa.int/web/guest/swarm/data-access))) (accessed on 18 October 2024),\" were used to calculate fluctuations or perturbations in the Ne profile (dNe), following the methodology outlined by [PERSON] et al. 2014 [42].  
\[\mathrm{dNe}=[\frac{1}{10}\sum_{i=1}^{10}(\mathrm{Ne_{i}-Ne_{oi}})^{2}]^{1/2} \tag{1}\]  
\(\mathrm{Ne_{i}}\) and \(\mathrm{Ne_{oi}}\) are the original Ne and low-pass filtered Ne (with a cut-off period of 10 s) at the ith data point.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 5 Critical Analysis and Open Research Challenges
### Future Research Directions
#### 5.8.3 Cross-Domain IM [243; 244]  
Develop integrated frameworks that consider aerial, terrestrial, and satellite interference, enabling coordinated decisions across all layers of the B5G ecosystem.