esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Results
### Abrasion Resistance  
Figure 7 shows the abrasion resistance of the HFLMC mixtures for emergency pavement repair. Compared to mixtures without fibers, those containing PVA, jute, nylon, jute-nylon, PVA+jute, and PVA+nylon fibers showed 7.31%, 10.64%, 7.30%, 16.95%, 19.51%, and 22.60% increases in abrasion resistance, respectively. This is because the fibers prevent particles from breaking free of the concrete surface. Furthermore, the fibers suppressed the occurrence and growth of surface microcracks and created denser concrete structures [2; 3]. The abrasion resistance of HFLMC was much greater than that of the plain mixtures. As with the other mechanical properties, the hybrid fiber system was more effective than the single-type fiber-reinforced system at suppressing the formation and growth of cracks within and on the surface of the concrete. Notably, the PVA+nylon hybrid fiber-reinforced mixture exhibited the greatest resistance to abrasion.
# Effect of Laser Parameters on Optical Stealth Transmission System Performance
## 4 System Simulations and Discussion
### BER Performance  
In order to quantitatively analyze the influence of laser parameters on the system performance, the BER performance of the optical stealth transmission system is studied. With different modulation frequencies, the BER curves are shown in Figure 8a. The bias current is 0.08 A and the modulation current is 0.2 A. The BER performance deteriorates with the increase of modulation frequency. The stealth signal can be transmitted error-free over the public channels at 1.25 GHz and 2.5 GHz. Figure 8b shows BER curves of one public channel with stealth signal. The change of the modulation frequency of stealth signal has little effect on the BER performance of public channel. It is verified that the stealth channel has good concealment. After optimizing the laser parameters, the BER results are shown in Figure 8c. Both 10 Gbps and 5 Gbps stealth channel can be transmitted error-free. The bias current is set to 0.13 A and modulation current is set to 0.12 A for the 10 Gbps stealth channel, while the bias current is set to 0.2 A and modulation current is set to 0.1 A for the 5 Gbps stealth channel. However, as shown in Figure 8c, the stealth signal is a clear optical frequency comb in spectral domain. This means the stealth channel has poor concealment in the spectral domain. In addition, it indicates that there is a trade-off between BER performance and covertness of the stealth channel. In order to ensure the spectral covertness of stealth channel, frequency chirp and time jitters of laser outputs are inevitable, and the time jitters will lead to the degradation of BER performance.  
The BER performance of stealth channel with different bias current is also investigated and the BER curves are shown in Figure 9. As bias current increases, the BER decreases. However, when the bias current increases above 0.2 A, the BER will increase. In addition, the high bias current results in the generation of optical frequency comb and the increase of TNR in the spectral domain. Therefore, the bias current of stealth channel should be optimized in terms of transmission performance and covertness. In terms of covertness,  
Figure 7: **(a,b)** The optical spectrum of public signal, (**c,d**) the waveform of stealth channel, and (**e,f**) the eye diagram of public channel with different bias currents.  
the bias current should be decreased to make the TNR less than 3 dB. In the aspect of transmission performance, channel coding for the stealth channel can be used to reduce the BER; especially, the forward error correction schemes employ iteratively decipherable codes. Due to the superiority of decoder complexity and BER performance, low-density parity-check (LDPC) codes [14; 15; 16] might be good candidates to significantly improve the BER performance of the stealth channel.  
Figure 8: BER performance of (**a**) stealth channel and (**b**) public channels under different frequencies, (**c**) optimum BER results; (**d**) the corresponding optical spectra.  
Figure 9: BER of stealth channel with different bias currents.
# A Flexible FPGA-Based Stochastic Decoder for 5G LDPC Codes
## 6 Implementation Results and Discussion
### Results
#### 6.2.2 Hardware Utilisation  
The proposed design's advantage lies in the significant reduction in decoder complexity. Table 5 compares the suggested design and the min-sum-based decoder architectures, indicating that the former requires less hardware by approximately 37%. A crucial parameter that affects the decoder's area and routing complexity is the number of interconnecting wires between its nodes. In this regard, the suggested design outperforms the min-sum-based decoder by requiring 34,729 fewer interconnect wires. Other parameters of the suggested design are comparable to those of the min-sum-based decoder. The results in Table 6 indicate that the increase in coding rate significantly impacts the SD decoder's  
Figure 8: BER plot of various algorithms and code rates for \(N\) = 3808.  
decoded processing throughput. However, at the same time, it negatively affects the error correction performance.
## 2 Methodology of the Forward Modelling
### AVA Seismic Response Theoretical Solution  
To validate the accuracy of the AVA seismic response simulated using the new propagation matrix theory, we derived a theoretical solution for the AVA seismic response based on the propagation matrix theory, assuming that the incident angle of the reflection coefficient at the \(iz\)-th reflection interface in the subsurface medium is \(\theta\) and the reflection coefficient generated on the ground is \(r^{iz}\). The direct calculation of the reflection coefficient using the propagation matrix includes the seismic response generated by the first and last reflection interfaces on the ground. To compute only the reflection coefficient generated by the \(iz\)-th reflection interface on the ground, the reflection coefficient on the upper side of the \(iz\)-th reflection interface is denoted as \(\mathbf{r}_{1,iz-1}\), and the reflection coefficient on the upper side of the \((iz+1)\)-th reflection interface is denoted as \(\mathbf{r}_{1,iz}\).  
\[(\mathbf{A}_{1}-\prod_{i=1}^{iz-1}\mathbf{B}_{i}\mathbf{B}_{iz}\mathbf{A}_{2,iz})\mathbf{r}_{1,iz- 1}=-\mathbf{i}_{\text{P}}, \tag{25}\]  
\[(\mathbf{A}_{1}-\prod_{i=1}^{iz}\mathbf{B}_{i}\mathbf{B}_{iz+1}\mathbf{A}_{2,iz+1})\mathbf{r}_{1, iz}=-\mathbf{i}_{\text{P}}, \tag{26}\]  
\[\mathbf{r}^{iz}=\mathbf{r}_{1,iz}-\mathbf{r}_{1,iz-1}. \tag{27}\]  
The reflection coefficients \(\mathbf{r}^{iz}(1,1)\) and \(\mathbf{r}^{iz}(2,1)\) represent the reflection coefficients generated by the \(iz\)-th layer for P-wave and S-wave surface reflections, respectively. The seismic response generated by the \(iz\)-th layer on the ground can be obtained by convolving the reflection coefficients with the Ricker wavelet. The theoretical solution of the AVA seismic data with an incident angle of \(\theta\) can be obtained by accumulating the reflection coefficients of the \(nz\)-1 layers, as follows:  
\[\mathbf{d}=\mathbf{W}\sum_{1}^{nz-1}\mathbf{r}^{iz}, \tag{28}\]
# EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs
## 3 Energy-Aware Grid-Based 3D-Obstacle Avoidance (EAOA)
### Preliminaries  
In our previous work [14], the area is divided into grid-cells. Each grid-cell has a rectangular shape and covers a portion of the area. The UAV footprint has a circular shape and is projected to the area portion below the drone. Hence, each grid-cell represents the inscribed rectangle in the UAVprojected footprint. The original planned path starts with the longest grid-side and crosses the center of the cells (see Figure 2a). It is plotted using the 2D top-view area scanning and is generated offline.  
We denote \(C_{\chi}^{t}\) a grid cell in this figure and \(P_{\chi}\) the perpendicular plane to \(C_{\chi}^{t}\) which passes by the center of the cell. Hence, two consecutive planes \(P_{\chi}\) and \(P_{\chi+1}\) on the same grid column are h unit apart, where h is the length of \(C_{\chi}^{t}\). In this work, we assume the obstacles positions are fixed and known and we want to avoid them using 3D information. We lean on the original path in Figure 2a. The drone will follow it, and, right before the cell that contains the obstacle, an image for the front area is captured in order to decide later about the next drone's position. The captured image frame is to be defined in the coming section.  
To illustrate, in Figure 2b, we assume having an obstacle in plane P2. The red line represents the original path in Figure 2a. The drone re-plans the path and changes it dynamically according to the locations of the obstacles. The green line represents the new trajectory. The goal of this work is to determine the new position of the drone while keeping the total trajectory path and energy consumption to a minimum.  
Figure 1: Different ways in avoiding obstacles.  
Figure 2: Illustration of the top and frontal views. (**a**) top view: a binary image representing an area of interest [14]: black cells are obstacles, grey cells are free areas; (**b**) example of a frontal view with obstacle. The red line is the original path and the green one is the new path.  
The main phases in our work are: (1) Determining the frame dimensions in the front view, (2) locating the next drone's position.
# A Parallel-Phase Demodulation-Based Distance-Measurement Method Using Dual-Frequency Modulation
## 7 Conclusions  
In this paper, an AMCW-based ToF measurement method using parallel phase demodulation and dual-frequency modulation is proposed. The performance of this proposed method was also verified through experimental results. According to the results, the proposed method presents relatively low standard deviation of distance measurement due to zero DC components of demodulation signal. In addition to improvement of accuracy, time for single distance measurement can be reduced by the proposed method. Moreover, using the dual-frequency modulation, the proposed method can extend unambiguous range effectively, as shown in experimental results. The proposed method will be applied to a compact scanning-type 3D sensor for robot applications in the author's group.  
**Author Contributions**: Conceptualization, [PERSON]-[PERSON]; methodology, [PERSON]-[PERSON]; software, [PERSON]-[PERSON]; validation, [PERSON]-[PERSON] and [PERSON]-[PERSON]; formal analysis, [PERSON]-[PERSON] and [PERSON]-[PERSON]; investigation, [PERSON]-[PERSON] and [PERSON]; resources, [PERSON] and [PERSON]-[PERSON]; data curation, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON]-[PERSON] and [PERSON]-[PERSON]; writing--review and editing, [PERSON]-[PERSON], [PERSON]-[PERSON], and [PERSON]-[PERSON]; visualization, [PERSON]-[PERSON] and [PERSON]-[PERSON]; supervision, [PERSON]-H.P.; project administration, Y.-H.P.; funding acquisition, Y.-H.P. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Modulation Frequency** & **Unambiguous Range** \\ \hline
34.375 MHz & 4.363 m \\
31.25 MHz & 4.8 m \\
21.875 MHz & 6.857 m \\
3.125 MHz & 48 m \\
31.25 MHz + 34.375 MHz & 48 m \\
21.875 MHz + 34.375 MHz & 48 m \\
3.125 MHz + 34.375 MHz & 48 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Unambiguous ranges of each frequency combination  
Figure 9: (**a**) Bias error for each frequency combination; (**b**) standard deviation for each frequency combination.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This work was supported by the \"Human Resources Program in Energy Technology\" project of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial resources from the Ministry of Trade, Industry, and Energy of the Republic of Korea (number. 20184030202000). This work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (number 2017R1A2B2010759).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Estimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment
## 5 Experiment Results and Analysis
### Turning Driving Condition at Low Speed
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & **Max Absolute Error (Rad)** & **Max Relative Error (Rad)** & **Mean Absolute Error (Rad)** & **Mean Relative Error** & **Standard Deviation** \\ \hline UKF & 0.3619 & 89.57\% & 0.0530 & 49.47\% & 0.0732 \\ AN-STUKF & 0.1305 & 46.11\% & 0.0201 & 17.24\% & 0.0453 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Error analyses for small radius turning. Figure 11: (a) A vehicle turns in a small radius; (b) a vehicle drives straight. Figure 12: The sideslip angles of the vehicle body. Here, we define the instantaneous center of rotation (ICRs) as (41):  
\[\left\{ \begin{array}{c}x_{ICRv}=-\frac{\dot{y}}{\dot{\phi}}\\ y_{ICRv}=\frac{\dot{x}}{\dot{\phi}}\end{array} \right. \tag{41}\]  
where \(x_{ICRv}\) and \(y_{ICRv}\) are defined in Figure 1, which are expressed as the coordinate values of vehicle instantaneous center of rotation on the \(x\)-label and \(y\)-label, respectively. The data from 20 to 40 s is chosen to calculate the ICRs shown in Figure 13 to analysis the vehicle kinetic characteristics, because, in this time period, the vehicle is moving at a relatively high rotating rate. The ICRx (whose average value is about 3 m) is larger than the distance between the first axis (\(a=1.836\) m, which is shown in Table 1) and the CM of the vehicle, which marks that the ICR of the vehicle is ahead of the first axis, as shown in Figure 14. The ICRy shows the ICR of the vehicle is located on the left side of the vehicle, which performs similarly with the rotating direction. The ICRs shifting forward is an important phenomenon in skid-steered wheeled vehicle turning. The authors of [14; 48] discuss the phenomenon of ICRs shifting forward in large radius turning conditions. The ICRs moving forward reflect the increasing of sideslip angle of the vehicle body, which leads to the instability of the vehicle. The ICRs moving forward also reflects that all the lateral tire force point to the direction of the rotating center. When the ICRs are between the first and rear axis, the sideslip angle of the first axis tire is still positive. This phenomenon reflects that the vehicle needs large lateral tire force to satisfy the lateral motion. Figure 14: The ICR of the skid-steered wheeled vehicle when it turns at low speed and in a small radius. Figure 13: The instantaneous center of rotations (ICRs) of the vehicle when the vehicle turns.
## 1 Introduction
Existing methods of measuring noise in human speech divide the acoustic signal into two parts: a harmonic and a noise component. Based on this assumption, estimates of the harmonic-to-noise ratio (HNR) have been calculated. We expect that adding a HNR to the voiced and unvoiced components that involve the presence of noise in voiced frames, the quality of synthesized speech in the noisy time regions will be more accurate than the baseline [31]. This method has a twofold advantage: (a) it makes it possible to eliminate most of the noise residuals; and (b) it attempts to reproduce the voiced and unvoiced (V/UV) regions more precisely, that is, it resembles natural sound signal-based TTS synthesis. The goal of this article is to further improve our earlier vocoder [31] for high-quality speech synthesis. Specifically: (a) it proposes three adaptive techniques that enhance the performance of continuous F0; (b) it studies adding HNR as a new excitation parameter to the voiced and unvoiced segments of speech; and (c) it explores a different methodology for the estimation of MVF. We will finally show that the performance of the proposed vocoder is superior to state-of-the-art vocoder performance (the one based on STRAIGHT) in synthesized speech. This paper is organized as follows: In Section 2, continuous F0 and three refinements methods are described. Then, the new form of continuous vocoder is presented in Section 3. Experimental setup with measurements metric is defined in Section 4. Objective and subjective evaluations are discussed in Section 5. Finally, in Section 6, we conclude this paper with a brief summary.
## 5 Conclusions  
### Cooling Characteristics of HICSs  
In this study, the cooling characteristics of HICS were explored under different spatial and temporal conditions. Under low SD and with meteorological conditions affected by solar radiation, there was a stronger cooling effect and greater diurnal variation in cooling. Additionally, the cooling effect was stronger in the morning than in the afternoon.  
UGSs and GBAM zones reduced the UHI by transforming urban land cover features into components that create low temperatures. The cooling effect of UGSs in the SVF range of 0.0-0.2 (i.e., the highest SD range) was \(-\)1.678 to \(-\)0.025 \({}^{\circ}\)C (Table 4). In GBAM zones, the cooling effect in the SVF range of 0.0-0.2 was 0.003 to 1.167 \({}^{\circ}\)C at all TPs (Table 5). An effect value of \(\geq\) 0 \({}^{\circ}\)C or larger implies no effect compared with non-HICS conditions. This finding indicates that GBAM zones had no cooling effect under high-SD conditions. The basic GBAM mechanism involves suppression of temperature elevation by reducing the amount of solar radiation absorbed through a coating of high-albedo materials [8]. However, a lower SVF causes a smaller amount of solar radiation to reach the floor surface because of the effects of surrounding terrain features [50]. With respect to GBAM zones, where solar radiation is blocked, it is likely that no cooling effect occurred because a small amount of solar radiation reached the covering [39]. Conversely, in UGSs, there was a lower cooling effect in low-SD environments; in contrast to GBAM zones, UGSs produced an effect value \(<\) 0 \({}^{\circ}\)C. UGSs were characterized by the effect of blocking solar radiation as well as the cooling effect of vegetation cover via evapotranspiration [3]. Thus, UGSs had a temperature-reducing effect under high-SD conditions, whereas GBAM zones did not.  
The basic principle of shadow zones in UGSs is to reduce the surface temperature by blocking solar radiation [10; 11]. When meteorological conditions were less affected by solar radiation, there was minimal need to block solar radiation; a cooling effect value of \(\sim\)1 \({}^{\circ}\)C was achieved under all SD conditions and times except for 11:00 and 13:00 in the SVF range of 0.8-1.0. In contrast to UGSs and GBAM zones, the cooling effect was weak. When meteorological conditions were affected by solar radiation, a cooling effect of \(-\)5.319 to \(-\)0.913 \({}^{\circ}\)C was observed under all SD conditions at 9:00, 11:00, and 13:00, indicating a stronger cooling effect than when meteorological conditions were less affected by solar radiation (Table 6). When morning and afternoon were compared, the cooling effect was greater in the morning than in the afternoon, with a minimum value of \(-\)3.065 \({}^{\circ}\)C (SVF 0.6-0.8) and a maximum value of \(-\)5.625 \({}^{\circ}\)C (SVF 0.8-1.0) under all SD conditions (Table 6). Thus, shadow zones in UGSs were more strongly affected by diurnal changes and weather conditions (i.e., temporal conditions) than by SD (i.e., spatial conditions).
## 2 The Effects of PCC on Biological Samples
### In Vitro Experiments
#### 2.2.2 Results  
The histological analysis performed on blood smear after PCC treatment has showed both platelets aggregation and fibrin polymerization, indicating a pro-coagulant effect of the plasma source on the blood, even ex-vivo, with all the setting tested. Indeed, 60\({}^{\prime\prime}\) at low intensity were sufficient to induce platelet aggregation and fibrin polymerization (Figure 2B) and the percentage of aggregation detected was proportional to the treatment intensity (Figure 2C,D). Despite the remarkable result observed for the blood coagulation, some concerning results appeared with the increase of intensity; after 60\({}^{\prime\prime}\) at STD conditions, red cell lysis begins (Figure 2C) and rapidly increase with longer treatments (data not shown). Also, 60\({}^{\prime\prime}\) of high intensity treatment cause a dramatic platelet aggregation and fibrin polymerization but also a robust red cell lysis. Of note, following longer treatments (more than 90\({}^{\prime\prime}\)) the samples volume was strongly reduced, suggesting that, as expected, the heat developed has the tendency to dry the sample and possibly play a role in the coagulation process. Moreover, high intensity treatment also display peculiar black colored spots at the platelet clot areas, possibly due to the accumulation of iron released by the red cells.  
Figure 2: Blood Coagulation Analysis in vitro. Representative images of H/E staining on blood smears. (**A**) CTRL (no treatment). (**B**) 60\({}^{\prime\prime}\) LOW intensity treatment. (**C**) 60\({}^{\prime\prime}\) STD intensity treatment. (**D**) 60\({}^{\prime\prime}\) HIGH intensity treatment. The arrows indicate some platelet aggregates and fibrin networks.
## 5 Results and Discussion
### RQ1: What State-of-the-Art Methods Exist for Optimizing LoRaWAN Energy Efficiency?
#### 5.1.1 State-of-the-Art RL Techniques  
RL techniques are utilized across the literature that focus on optimization of resource allocation, and EE is LoRaWAN networks. Table 4 provides a summary of the techniques used to answer RQ1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author (s)** & **Year** & **Technique Used** & **Model** & **Application** \\ \hline [PERSON] et al. [36] & 2020 & DRL & Multiple DRL Agents & \begin{tabular}{c} LPWA networks such as LoRaWAN, \\ wireless communication \\ environments for the IoT. \\ \end{tabular} \\ [PERSON] et al. [37] & 2021 & RL & \begin{tabular}{c} Stochastic Discrete \\ Approach \\ \end{tabular} & \
\begin{tabular}{c} Development of an adaptable \\ LoRaWAN strategy for \\ industrial applications. \\ \end{tabular} \\ [PERSON] et al. [38] & 2021 & DQN & Deep Q-Network (DQN) &
\begin{tabular}{c} Lifespan of the LoRa \\ Class A end-nodes \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of RL techniques.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author (s)** & **Year** & **Technique Used** & **Model** & **Application** \\ \hline [PERSON] et al. [32] & 2020 & DL & ANNs & Optimization of transmit power \\ [PERSON] et al. [33] & 2022 & DL-based Resource Allocation & GRU & Resource management in large-scale \\ [PERSON] and [PERSON] [34] & 2023 & DNN AI-ERA approach & DNN &
\begin{tabular}{c} LoRa-enabled device deployments \\ resource assignment issue in static and \\ mobile IoT applications \\ \end{tabular} \\ [PERSON] et al. [35] & 2023 & ML & NN & Indoor localization within LoRaWAN \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of DL and NN techniques.