esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Factors for Degrading Performance of Localization
### NLoS Events  
Non-line-of-sight (NLoS) events refer to situations where the direct path between the anchor and the device is obstructed by an object, such as a building or a car. In such cases, the wireless signal is reflected or diffracted around the obstacle and reaches the receiver via a different path, not the direct path. This can cause problems in localization performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Comparison Aspects** & **Proximity** & **Dead Reckoning** & **Fingerprinting** \\ \hline Accuracy & Moderate & Moderate & High \\ \hline Energy Efficiency & High & High & Low \\ \hline Cost & Low & Moderate & High \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between range-free techniques.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Comparison Aspects** & **RSS** & **ToA** & **TDoA** & **AoA** \\ \hline Accuracy & Moderate & High & High & High \\ \hline Attenuation & High & Low & Low & Moderate \\ \hline Cost & Low & Moderate & Moderate & High \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between range-based techniques.  
As a main problem, NLoS can lead to multipath. Multipath refers to the propagation phenomenon that results in radio signals reaching the receiver by two or more paths, which occurs when wireless signals are reflected from surrounding surfaces and arrive at the receiver through many ways. This causes phase and amplitude distortion, interference, and signal fading, leading to inaccuracies in localization performance.  
Another problem is the difficulty in accurately predicting and modeling the behavior of the reflected signals. The reflected signals are affected by the shape and location of the obstacles, as well as the frequency and polarization of the signal. Therefore, it is difficult to predict the exact path of the reflected signals, which can lead to localization errors.
# Experimental Study on Fluid Dissipation Effects in Core Samples by NMR Measurement
###### Abstract  
Laboratory core nuclear magnetic resonance (NMR) relaxation measurements offer geological information, including rock porosity and oil saturation, relevant to logging. When core samples drilled from wells are exposed to air, the fluids within their pores inevitably dissipate. This phenomenon may lead to discrepancies between the results of nuclear magnetic resonance relaxation experiments and the actual situation underground. To deeply explore the impact of fluid dissipation on NMR core analysis experimental results, a series of simulated dissipation experiments were designed under constant temperature and humidity conditions. Variations in one-dimensional and two-dimensional NMR measurement results of oil-saturated samples were examined under varying crude oil viscosities and dissipation times. The experimental results indicate that as exposure time increases, the T\({}_{2}\) distribution of oil-saturated cores decreases, and the amplitude of the T\({}_{2}\) distribution peaks decreases. Both oil and water relaxation components show a decreasing trend; however, the dissipation rate of the bounding water component significantly exceeds that of the crude oil component. By employing two-dimensional NMR relaxation time distribution fluid quantitative analysis technology, the relationship between the dissipation rates of various phase fluids and exposure time during the stable dissipation stage was analyzed. This offers a reference for adjusting the oil saturation of exposed cores based on NMR measurements.  
core analysis; T\({}_{1}\)-T\({}_{2}\); fluid dissipation; crude oil viscosity +
Footnote †: journal: applied sciences
## 2 Materials and Methods  
### Materials Fabrication  
The initial materials to prepare Al-Mg\({}_{2}\)Si-B\({}_{4}\)C hybrid composites were pure aluminium (99.9% purity), pure magnesium (99.9% purity), pure silicon (98.9% purity), and pure B\({}_{4}\)C ceramic powder with a size of 10-30 \(\upmu\)m. In order to fabricate the hybrid composites, Al ingot was melted at 800 \({}^{\circ}\)C using an induction furnace, and then Si was added to the molten metal at the same temperature. After dissolving the Si in Al, the melt temperature was reduced to 750 \(\pm\) 10 \({}^{\circ}\)C and pure magnesium was then introduced to the melt. The temperature was reduced to prevent the oxidation of Mg. After homogenization and removal of the drops, the melt was poured into a steel mold to produce the composite ingot. To fabricate the hybrid composites, 2 kg of composite ingot was melted in a resistance furnace and then, B\({}_{4}\)C particles (2.5 wt%) were added gradually into the Al-Mg-Si melt. The melt was stirred constantly at a speed of 500 rpm for 15 min at 750 \({}^{\circ}\)C to obtain a uniform dispersion of reinforcement particles in the molten metal [23; 24]. In fact, due to high viscosity of the composite melt, long stirring time is needed to achieve good mixing of the reinforcement particles inside the melt. The hybrid composite melt was cast in the pre-heated (220 \({}^{\circ}\)C) cast iron die to solidify. The procedure was repeated to prepare hybrid composites with 0, 5, and 10 wt% B\({}_{4}\)C. The fabricated samples were used for microstructure examination, mechanical tests, and wear tests.
## 4 Engineering Example Application
### External Factors Affecting Slope Displacement  
Among the natural factors that influence slope stability, rainfall, water level, earthquake, and wind play significant roles. Since the effects of earthquakes and wind are minimal for the Three Gorges Xin Tan landslide examined in this paper, and considering that the Xin Tan landslide is situated along the Yangtze River, which typically experiences higher rainfall and increasing water levels with the season, this study focuses on investigating the effects of rainfall and water level as natural factors on the Xin Tan landslide.
# Feasibility Test and Analysis for Heavy Haul Freight Using WDPS Based on MIMO-OFDM
## 3 Measurement and Test
### Braking Performance Measurement and Analysis  
In Korea, this test was the first to verify WDP and heavy-haul freight (it is approximately double existing capacity) on a running line. For safety reasons, when 50 freights were connected, it should be first confirmed whether service braking and emergency braking performance are normally applied in the field. We experimented in two cases: one was a centralized control where one locomotive controls 50 cars, and the other was a distributed control where two locomotives control each. In both cases, when braking command, the pressure and time at the BP and the BC in the locomotive were measured. Those at the BC in the 1 st, 10 th, 20 th, 30 th, 50 th freight were measured [24]. As shown in Appendix B, Appendix C and the Table 2 below, the FSB test result, the brake equivalent response time is 16.6 s shorter than the centralized control, and the braking release time is 16.6 s shorter. Additionally, the EB test result confirmed that the brake equivalent response time is 2.3 s and the braking time is 25.8 s shorter than the centralized control. That is, in the distributed control of the heavy haul freight, the master locomotive at the front and slave locomotive at rear control the BP pressure at the same time, so that the braking action starts from the freight close to the locomotive. As a result, the transport capacity increased to double, and the braking and release time was also reduced by about 1.5 times.
# The Impact of Inter-Modulation Components on Interferometric GNSS-Reflectometry
## 2 Characteristics of the Multiplexed Signals
Similar to the partial interferometric processing method in [25], the in-phase and quadrature components of the beamformed signals are separated through the carrier phase tracking loop. By cross-correlating the extracted signal components from both sub-arrays, the waveforms of the GPS L1 in-phase and quadrature components (WF-I and WF-Q) from PRN01 have been produced separately, as shown in Figure 4. The existence of a wideband component in the quadrature component waveform can be noticed by the sharp ACF, compared to a pure C/A code ACF (see Figure 4). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Signal** & **Carrier Phase** & **Modulation** & **Percentage of Power** \\ \hline C/A code & Q & BPSK(1) & 25\% \\ P code & I & BPSK(10) & 18.75\% \\ M code & I & BOC(10, 5) & 31.25\% \\ IM & Q & BOC(10, 10) & 25\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main characteristics of the signal components transmitted by GPS Block IIR-M satellites at L1 band computed from [22]. Figure 3: Power spectral density (PSDs) and Woodward Ambiguity Functions (WAFs) of GPS L1 composite signal by considering the IM component. (**a**) PSDs of the composite signal with and without considering the IM component; (**b**) Squared ACF of composite GPS L1 signal with and without considering the IM component.
## 2 Materials and Methods
### MP Identification and Quantification
#### 2.4.2 SEM-EDS Analysis  
The surface morphology of the MPs was determined by applying SEM. Each sample was coated with 5 nm of chromium prior to analysis utilizing a Quantum Design Q150T Plus sputter coater (Darmstadt, Germany). SEM images were acquired using a Thermo Fisher Pharos Phenom G2 Field Emission Scanning Electron Microscope (Waltham, MA, USA), which was operated under high vacuum (0.1 Pa) with the accelerating voltage set at 15 kV, while both backscattered and secondary electron detectors were employed at a ratio of 20:80. Also, the surface chemical composition of the MPs surface was studied through EDS analysis using the same instrumentation.
## 3 Methodology and Data Collection
### Traffic Data  
The traffic data required are the average annual daily traffic (AADT) of each route in the different sections along its entire length. In Mexico, the SICT has installed and operates a system of traffic measuring, which keeps macroscopic traffic variables updated annually, such as volumes and speed information. Traffic data also allows vehicle classification (Table 1), according to the percentages extracted from the highway traffic data of the SICT [42]. Figure 2: Transport road corridors in Mexico. Source: Own elaboration using Google Maps. The vehicle fleet data provide detailed information for calculating and estimating speeds, operating costs, travel time, and other aspects. It is necessary to also identify the number of passengers, number of wheels and axles, annual kilometers, average vehicle life, working hours and weight, and other data. The characterization was carried out according to the studies of the Origin and Destination Survey (ODs) that apply the SICT to the country's roads, which also includes the weights and dimensions of vehicles and the age of the vehicles traveling on the roads. Since the stations for the implementation of the surveys are located at different points every year, it is not possible to apply for a single study. Therefore, the present work decided to use the last results of the monitoring station located on each transport corridor [44]. A summary of the studies to determine the age of the vehicle fleet is shown in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**FHWA Classification [43]a** & **Type of Vehicle** & **SICT Classification [42]b** \\ \hline Class 2 and 3 & Light Vehicle & A \\ \hline Class 4 & Bus & B \\ \hline Class 5 & Medium truck (2 axes) & C2 \\ \hline Class 6 & Heavy truck (3 axes) & C3 \\ \hline Class 9 & Articulated truck (5 axes) & T3-S2 \\ \hline Class 10 & Articulated truck (6 axes) & T3-S3 \\ \hline Class 13 & Double articulated truck (9 axes) & T3-S2-R4 \\ \hline \end{tabular} Source: a Data from FHWA (2014) [43] and b from the SICT (2020) [42].  
\end{table} 
Table 1: Vehicle Classification.
# AA-HMM: An Anti-Adversarial Hidden Markov Model for Network-Based Intrusion Detection
## 3 Foundation
### Local Optimal Window Width  
To determine the correlation between the WW (length of section) and the corresponding accuracy, we performed extensive experiments on a variety of data sets (e.g., NSL-KDD, CTU-13, etc.): (1) building and testing nearly 20 HMMs (refer to [24; 25] for the principle of HMM) with the same initial configuration on the same data sets; (2) each model sets up a unique WW--if a model's WW is25 and a data set has 1000 samples, then this model will split the data set into 40 sections, each one containing 25 samples, and making predictions on the 40 sections (windows) in order. (3) The range of the tested WWs was 25 to 450 (steps of 25). Figure 1 provides a representation of the relationship between these models with different WWs and their corresponding accuracies.  
For the previous inference regarding the PE, longer WWs usually suffer from higher PEs and are harder to accurately predict, so the models with wide WWs (from 275 to 450) have low accuracies. Although the models with narrow WWs (from 10 to 100) are also inaccurate, this phenomenon does not contradict the inference of PE, but results from another vital factor called Pattern Variation (PV), which is understood as the samples' differences in terms of type and distribution between different windows. Since the HMMs interpret samples as hidden states and observations, the PV should be expressed as the differences between hidden states, as well as the differences between observations, in terms of type and distribution between adjacent windows. Therefore, a higher PV would produce a lower detection rate because it is difficult for a model with a fixed configuration to perform well on all windows (patterns). Consequently, the WWs that result in relatively high accuracies are called Local Optimal Window Widths (LOWWs). As Figure 1 shows, the LOWWs for this experiment were 125, 150, 175, 200, 225, and 250 samples in length. So, the next task for improving performance was to search for one of the LOWWs and set it as the model's WW.
# MIMO Channel Estimation in an SDR Platform for Evaluation of D&F Relay Nodes
## 6 Numerical Results and Performance Analysis
### Computational Complexity of the Algorithms  
This subsection analyzes the computational complexity of the presented algorithms, in terms of arithmetic operations. We counted the number of operations required to carry them out, as a function of the dimensions of the vectors and matrices involved.  
In the case of the LS estimator, the calculation of \(\mathbf{\hat{H}}_{p}\) given by the expression (5), involves the product of the matrices \(\mathbf{P}_{p}^{H}\in\mathbb{C}^{\alpha\times\lambda}\) and \(\mathbf{Y}_{p}\in\mathbb{C}^{\lambda\times\alpha}\) for the \(\mathrm{N}_{Tx}\times\mathrm{N}_{Rx}\) antennas combined, where \(\lambda\times\alpha=P\) (the total number of pilot symbols). Table 3 summarizes the arithmetic operations that the presented channel estimator requires to determine the channel frequency responses of the pilot symbols.  
As explained in Section 4.1, we implemented three interpolation algorithms to calculate the channel frequency responses of the data symbols. Considering the expressions (6) and (8), and Algorithm 1, we determined the number of arithmetic operations, as shown in Table 4. From Table 4, one can conclude that, in terms of the required arithmetic operations, the BNNI method is simpler, compared to BLI and BCI. The parameters \(\mu\) and \(P\) represent the total number of data symbols and the total number of pilot symbols, respectively.  
Figure 11: Comparison of throughput versus SNR for both modulations with LS-BCI-MMSE techniques.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithm** & **Additions** & **Multiplications** & **Divisions** \\ \hline LS Estimator & \(\mathrm{N}_{Tx}\mathrm{N}_{Rx}\frac{\lambda\alpha(\alpha+1)}{2}\) & \(\mathrm{N}_{Tx}\mathrm{N}_{Rx}\frac{(\lambda-1)\alpha(\alpha+1)}{2}\) & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Arithmetic operations required by LS Estimator Algorithm.  
The computational complexities of the equalizers described in Section 4.2 are presented in Table 5, where \(N\) is the total number of subcarriers in the system. From the table, it can be seen that the computational complexity of the MMSE equalizer was higher than that of the ZF equalizer.
# PEiD: Precise and Real-Time LOS/NLOS Path Identification Based on Peak Energy Index Distribution
## 5 Experimental Evaluation
### Impact of Packet Number  
To evaluate the impact of different packet numbers, we recollect a dataset of 464 samples under the LOS and NLOS paths (keeping the transceivers stationary). Each contains 2000 packets received continuously. We select a specified number of packets from each sample and construct eight different datasets (i.e., 10, 50, 100, 200, 300, 500, 1000, and 2000). Eight models, respectively, are trained on these datasets with the same test ratio of 7:3. We plot their identification accuracy in Figure 8 (separated into LOS and NLOS two cases).  
The findings indicate a clear correlation between the number of packets and the identification accuracy. Initially, a modest increase in the packet number leads to a rapid enhancement in accuracy. Specifically, when only taking 10 packets into account, the model exhibits a low accuracy rate of 85.3% for LOS paths and 83.5% for NLOS paths. However, this accuracy improves significantly to 93.5% and 92.2%, respectively, when the number of packets increases to 200. The packet number of 200 tends to be a turning point. Even when the packet number expands tenfold, reaching 2000, the identification accuracy for LOS and NLOS paths only marginally increases to 94.3% and 97.5%, respectively. This incremental gain is noticeably less pronounced compared to the improvement observed between 10 and 200 packets.  
The previous observation motivates us to determine the optimal packet number. While increasing the packet number can effectively improve the model's identification performance, excessive packets can also introduce higher latency. Assuming a sampling frequency of 500 Hz, the collection of all 2000 packets for a single identification task would necessitate a delay of 4 s. This non-negligible latency significantly increases the time cost to build a dataset. Given that the performance gains from increasing packets diminish beyond 200, we can mitigate latency by appropriately reducing the packet number. Consequently, we have chosen 300 as the default packet size to keep a balance. This configuration achieves an accuracy rate of 94% for LOS paths and 92.7% for NLOS paths while maintaining a brief time delay of 600 ms.
# A ROTI-Aided Equatorial Plasma Bubbles Detection Method
###### Abstract  
In this study, we present a Rate of Total Electron Content Index (ROTI)-aided equatorial plasma bubbles (EPBs) detection method based on a Global Navigation Satellite System (GNSS) ionospheric Total Electron Content (TEC). This technique seeks the EPBs occurrence time according to the ROTI values and then extracts the detrended ionospheric TEC series, which include EPBs signals using a low-order, partial polynomial fitting strategy. The EPBs over the Hong Kong area during the year of 2014 were detected using this technique. The results show that the temporal distribution and occurrence of EPBs over the Hong Kong area are consistent with that of previous reports, and most of the TEC depletion error is smaller than 1.5 TECU (average is 0.63 TECU), suggesting that the detection method is feasible and highly accurate. Furthermore, this technique can extract the TEC depletion series more effectively, especially for those with a long duration, compared to previous method.  
GNSS; ionosphere; equatorial plasma bubbles; detection method; ROTI 1  
Hong Kong, China; [EMAIL_ADDRESS]
# Effects of Sinusoidal Model on Non-Parallel Voice Conversion with Adversarial Learning
###### Abstract
For this, the speech analysis and synthesis system (also referred to as vocoder) is of principal significance. Many attempts have been made previously to construct a parametric vocoder to model voice signals. We can group the vocoders based on their implementations into three main categories: source-filter [20], phase [21], and neural complex [22] models. However, most of the recent VC literature has focused only on source-filter and neural models. This narrow focus and lack of phase approach has caused important gaps in the literature. Sinusoidal modeling is another signal processing technique that offers promise for improving voice system performance. It can be characterized by the amplitudes, frequencies, and phases of the component sine waves. It is also synthesized as the sum of several sinusoids that can generate high-quality speech. Concisely, voiced speech can be modeled as a sum of harmonics (quasi-periodic) with instantaneous phases. On the other hand, unvoiced speech, which is non-periodic, can be formed only by white noise (which means that the spectrum of the noise-like excitation is flat and the number of harmonics is 0). To the best of the authors' knowledge, a sinusoidal-based phase model has not been investigated yet in non-parallel VC. To address this challenge, hence, this work seeks to fill this gap in the literature by conducting the first study to accurately determine the effect of the sinusoidal model in non-parallel VC, which does not include parallel utterances or time alignment. The rest of this paper is structured as follows: Section 2 briefly introduces a related work that motivates our study. Section 3 gives details of the continuous sinusoidal model we used for speech synthesis, and Section 4 explains the novel method we used for non-parallel voice conversion. Section 5 discusses the experimental setup and evaluation results. Key points and future work are finally given in Section 6.