esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Diffusion-Theory Based Analysis of Experiments
#### 3.2.2 Absolute vs. Relative Flow-Coefficients in Phantoms  
Figure 10a shows extracted \(D_{B}\) coefficients in phantoms vs. the experimentally controlled pump flow rate. Each symbol-line shows data at fixed channel depth (squares: depth \(D_{0}\); triangles: depth \(D_{1}\); circles: depth \(D_{2}\); asterisks: depth \(D_{3}\)). The derived Brownian flow coefficients \(D_{B}\) decreased with increasing flow rates, when the flow channel depth was shallowest, while it increased with increasing flow, as the height of the liquid surface above the flow channel was raised. Figure 10b shows the extracted speed of the moving particles \(V\) from phantoms vs. changing fluid flow speed in the channel. Each symbol-line shows data for fixed channel depth (as in Figure 10a). The pump-flow rate, \(F\) shown in Figure 10a was converted to a fluid flow speed in the channel (as described in Section 2.1.3) using \(F=V_{cd}A\), where \(A\) was area of a tube with diameter equal to the inner diameter of the flow channel (0.16 cm). The derived speed of moving particles on the other hand increased with increasing fluid flow speed for the shallow channel depths (squares and triangles in Figure 10b). However, the derived flow speed did not increase linearly with pump flow rate in phantoms with deeper channels (circles and asterisks in Figure 10b).  
Since the fluid flow in the channel was well-controlled experimentally, we examined relative changes in flow coefficients in the phantoms with actively pumped flow (i.e., across \(F_{1}\)-\(F_{3}\)). Relative flow rates in experiments were calculated as ratio of the pump flow rates of \(F_{2}/F_{1}\) and \(F_{3}/F_{1}\) corresponding to relative flow rates of 2.5 and 4, respectively. Relative changes in phantoms (at each channel depth) were calculated by dividing derived flow coefficients by the flow coefficients obtained for phantoms  
Figure 9: Experimental data (symbols) and fits using Equation (2) for three different flow models (dash-dotted: Brownian; solid: Random; dashed: shear) for two different flow phantoms. (**a**) shows representative data in phantom \(D_{0}F_{0}\) (pump flow turned off) while (**b**) shows data from phantom \(D_{0}F_{3}\) (highest pump flow).  
at pump flow \(F_{1}\). These data are shown in Figure 10c (for the Brownian flow coefficient ratios) and in Figure 10d (for the shear-flow speed coefficient ratios). Figure 10c,d only show three flow speeds (at each depth) as it does not include data from phantoms when the pump was inactive.
## 2 Materials and Methods
### Reactive Oxygen Capture Assay  
Reactive oxygen species (ROS) were identified through scavenging experiments. In this study, TBA (2 mol/L) and CF (240 mmol/L) were used as scavengers for hydroxyl radicals (\({}^{\bullet}\)OH) and superoxide radicals (O\({}_{2}^{\bullet-}\)), respectively. FFA (120 mmol/L) was employed to quench \({}^{\bullet}\)OH and singlet oxygen (\({}^{1}\)O\({}_{2}\)) (\(k_{\text{O}_{2},\text{ FFA}}=1.2\times 10^{8}\) M\({}^{-1}\cdot\)s\({}^{-1}\), \(k_{\text{OH, FFA}}=1.5\times 10^{10}\) M\({}^{-1}\cdot\)s\({}^{-1}\)) [15]. After adsorption equilibrium was reached, H\({}_{2}\)O\({}_{2}\) and the corresponding scavenger were added, and the remaining steps followed the MB degradation experiment protocol. Additionally, the presence of \({}^{\bullet}\)OH, O\({}_{2}^{\bullet-}\), and high valent iron was verified using the spin-trapping agent DMPO, while the existence of \({}^{1}\)O\({}_{2}\) was confirmed using TEMP. Moreover, the reaction between high-valent iron species and methyl phenyl sulfoxide (PMSO) generates methyl phenyl sulfone (PMSO\({}_{2}\)), allowing for the identification of potential high-valent iron species through the consumption of PMSO and the formation of PMSO\({}_{2}\). During the experiment, a 0.1 mM PMSO solution was introduced into the system, and samples were collected at 0, 5, 15, 30, 60, and 120 min using a syringe. The samples were then filtered through a 0.22 \(\upmu\)m membrane and analyzed. Sodium thiosulfate (0.5 M) was used as a quenching agent, and the concentrations of PMSO and PMSO\({}_{2}\) were determined using high-performance liquid chromatography (HPLC).
# Redundancy Mitigation Mechanism for Collective Perception in Connected and Autonomous Vehicles
## 2 State of Art about Collective Perception for Connected and Autonomous Vehicle  
This section introduces an overview about CP applied to CAV. In addition, we introduce the state-of-the-art requirements and opportunities for CP in CAVs in terms of congestion control and redundancy mitigation; data selection and fusion for CAVs applications that may increase vehicle perception; and the safety aspect of CP applications. Based on a detailed study of the literature and state of the art of CP in CAVs, we classified existing works in different areas, such as communication, data selection and fusion, and privacy and security.
# Immersive Virtual-Reality System for Aircraft Maintenance Education: A Case Study
## 5 Results and Discussion
### Usability  
The second hypothesis of the work is related to the usability of the system, for which the SUS questionnaire was used. A SUS score of 68 or greater would be considered above average; in the data shown in Table 1, it can be seen that the virtual environment was evaluated with an 80. More than 95% (21/22) scored greater than this average. Eighty-five percent (18/22) indicated that the system was easy to use (with a score of 4 or 5), and ninety-five percent (21/22) indicated that various functions in this system were well integrated. No one indicated with a score higher than two that the system was unnecessarily complex (1/22) or inconsistent (0/22). Sixty-six percent (14/22) have felt very confident using the system.  
Regarding Hypothesis 2, the results show that the system seems accessible enough and generates enough confidence for users to integrate it into their practical training routines. Therefore, this hypothesis could be considered confirmed by the results of this study design.
## 3 Methodology
### Selection of Training Samples Based on Clustering
#### 3.4.3 Clustering Based on the Modified K-means Algorithm  
The K-means algorithm, as a classical clustering algorithm, is famous for its simplicity and strong clustering ability [33]. As mentioned in Section 3.4.2, we set the value of K as three to divide the binarization spectrograms into three clusters, meaning that there is a major cluster that contained the most binarization spectrograms, and the other two clusters represented two extremes that differed from the major cluster.  
However, the K-means algorithm is highly random because the initial seeds are selected randomly, making it easily plunge into local optima. To solve this problem, the initial seed selection is modified. The modified K-means algorithm is as follows:  
Figure 6: Progress of binarization and feature extraction.  
The modified seed selection method identifies three seeds that have the longest distance from each other, and can ensure that the differences between the three categories are maximized.  
The next step is to determine which categories are valid. The category that contains the most spectrograms is regarded as a valid category firstly. By experience, the amount of valid spectrograms occupies approximately more than 85% of the total. Therefore, for the other two categories, if the sum of them is less than 15%, then the spectrograms of the two categories are determined to be invalid; if the sum of them is larger than 15%, then only the smaller category is determined to be invalid.
# Performance Analysis of Troposphere Scattering Communication Channel with Chirp-BOK Modulation
###### Abstract
In this paper, by utilizing a chirp modulation scheme into a troposphere scattering communication system, the mathematic formulation of a chirp-BOK signal was introduced to describe the characteristics of the signal passing through troposcatter channels. Then, the averaged BER formula was theoretically deduced over a Rayleigh flat fading channel and frequency-selective fading channel, respectively. In order to investigate the suitable chirp-BOK parameters to deal with different fading parameters, a seven-path fading channel model was established with tap delays and multipath gains. Finally, the BER performance of the chirp-BOK system was simulated and analyzed, with the results showing that an adaptive optimal chirp configuration could offer some significant performance gains. The main contributions of this work are twofold. Firstly, the theoretical averaged BER formula of chirp-BOK modulation over a troposcatter fading channel was deduced and shown, and it was consistent with the corresponding simulation results over the established seven-path fading model. Secondly, for a troposcatter communication environment with a Doppler shift, multipath gain vectors and delays, optimal parameter configurations for chirp-BOK modulation were achieved to obtain some significant performance gains.
## 3 Methodology  
### Overview and Preliminary Information  
#### 3.1.1 Model Overview  
To predict pedestrian hazardous crossing intentions in areas with obscured fields of view, we constructed the IPVO-LSTM algorithm, as shown in Figure 2.  
First, 2D pose estimates of pedestrians were obtained by collecting videos of pedestrians crossing the street through a depth camera mounted on the roadside, and 3D pose estimates and action classifications of pedestrians were obtained by combining an image depth estimation technique and a pedestrian pose estimation technique, respectively.  
Second, the different 3D pose coordinates of pedestrians and the results of the action classification were then used separately in the pedestrian interference state feature module, pedestrian 3D pose feature module, pedestrian velocity feature module, and pedestrian observation state feature module to obtain different features of the pedestrians: the interference state features \(\textbf{Inter}_{t}^{i}\) between pedestrians closer to the camera and other pedestrians, the 3D pose features \(\textbf{Pose}_{t}^{i}\) of pedestrians closer to the camera, the velocity features \(\textbf{Velocity}_{t}^{i}\) of pedestrians closer to the camera, and the observation state features \(\textbf{Observe}_{t}^{i}\) for describing the traffic conditions in the blind zone of pedestrians closer to the camera.  
Finally, the above features were fused in our constructed ff-LSTM, and the pedestrian hazard crossing intention prediction module was used to determine whether the traveler had a hazardous crossing intention.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **3D Info.** & **View** \\ \hline L-CAS [24] & Yes & On-board \\ PIE [25] & No & On-board \\ JAAD [26] & No & On-board \\ KITTI [27] & Yes & On-board \\ ATC [28] & No & Bird’s-eye \\
**Ko-PER**[29] & **Yes** & **Bird’s-eye** \\ Stanford drone [30] & No & Bird’s-eye \\ Cityscapes [31] & Yes & On-board \\ ActEV/ & No & Bird’s-eye \\ VIRAT [32] & Yes & On-board \\ ApolloScape [33] & Yes & On-board \\ inD [34] & No & Bird’s-eye \\ Argoverse [35] & Yes & On-board \\ nuScenes [36] & Yes & On-board \\ Lyft Level 5 [37] & Yes & On-board \\ Waymo [38] & Yes & On-board \\ H3D [39] & Yes & On-board \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pedestrian intention prediction datasets.
## 4 Distributions of Path Delay, Path Attenuation, Normalized Doppler Shift of the Scattering Path
### The Distribution of \(\tau_{\text{DIF}}\left(t\right)\)  
The time variant CDF of the time delay of the scattering path relative to the LOS path can be calculated with Equations (37)-(41) [32], where \(P\left(T_{\text{DIF}}\left(t\right)\leq\tau_{\text{DIF}}\right)\) is the probability that the virtual random variable \(T_{\text{DIF}}\) observed at the moment \(t\) is not larger that the random path delay \(\tau_{\text{DIF}}\) observed at the moment \(t\), \(\tau_{\text{min}}\leq\tau_{\text{DIF}}\leq\tau_{\text{max}}\), and \(S_{T}\left(\tau_{\text{DIF}},t\right)\) is the area of the elliptical area determined by Equation (38). \(\tau_{\text{min}}\) is the minimum path delay of the scattering path obtained through tangency point between the minimum delay-determined ellipsoid and the earth, \(\tau_{\text{min}}=\tau_{\text{SPE}}\). \[ \begin{split}& F_{T_{\text{DIF}}}\left(\tau_{\text{DIF}},t\right)=P \left(T_{\text{DIF}}\left(t\right)\leq\tau_{\text{DIF}}\right)\\ &\quad=\iint\limits_{T_{\text{DIF}}\left(t\right)\leq\tau_{\text {DIF}}}f_{X_{\text{D}}Y_{\text{D}}}\left(x_{\text{D}},y_{\text{D}},t\right) \text{d}x_{\text{D}}\text{d}y_{\text{D}}\\ &\quad\approx\frac{1}{S\left(t\right)}\iint\limits_{T_{\text{DIF }}\left(t\right)\leq\tau_{\text{DIF}}}\text{d}x_{\text{D}}\text{d}y_{\text{D} }\\ &\quad\approx\frac{S_{T}\left(\tau_{\text{DIF}},t\right)}{S\left(t \right)}\end{split}  \tag{37}\]  
\[\text{c}\tau_{\text{DIF}}=\sqrt{x_{\text{D}}^{2}\left(t\right)+\left(y_{ \text{D}}\left(t\right)-y_{\text{R}}\left(t\right)\right)^{2}+z_{\text{R}}^{ 2}\left(t\right)}+\sqrt{x_{\text{D}}^{2}\left(t\right)+\left(y_{\text{D}} \left(t\right)-y_{\text{T}}\left(t\right)\right)^{2}+z_{\text{T}}^{2}\left(t \right)}-d_{\text{LOS}}\left(t\right) \tag{38}\]  
Thus \(S_{T}\left(\tau_{\text{DIF}},t\right)\) can be calculated with Equation (39) similar to Equations (6)-(8), as follows,  
\[S_{T}\left(\tau_{\text{DIF}},t\right)=\pi l_{l,T}\left(\tau_{\text{DIF}},t \right)l_{s,T}\left(\tau_{\text{DIF}},t\right) \tag{39}\]  
where  
\[ \begin{split} l_{l,T}\left(\tau_{\text{DIF}},t\right)& =\left[\text{c}\tau_{\text{DIF}}+d_{\text{LOS}}\left(t\right) \right]\cdot\left[2\,\left(\text{c}\tau_{\text{DIF}}+d_{\text{LOS}}\left(t \right)\right)^{2}-\mu_{1}\left(t\right)\right]^{-1}\\ &\quad\cdot\sqrt{-\left(\text{c}\tau_{\text{DIF}}+d_{\text{LOS }}\left(t\right)\right)^{2}+\mu_{2}\left(t\right)}\cdot\sqrt{-\left(\text{c} \tau_{\text{DIF}}+d_{\text{LOS}}\left(t\right)\right)^{2}+\mu_{3}\left(t \right)}\\ l_{s,T}\left(\tau_{\text{DIF}},t\right)&=\left[\text{ c}\tau_{\text{DIF}}+d_{\text{LOS}}\left(t\right)\right]^{-1}\left[\left( \text{c}\tau_{\text{DIF}}+d_{\text{LOS}}\left(t\right)-\mu_{4}\left(t\right) \right)\left(\text{c}\tau_{\text{DIF}}+d_{\text{LOS}}\left(t\right)+\mu_{4} \left(t\right)\right)\\ &\quad\cdot\left(\text{c}\tau_{\text{DIF}}+d_{\text{LOS}}\left(t \right)-\mu_{5}\left(t\right)\right)\left(\text{c}\tau_{\text{DIF}}+d_{\text{ LOS}}\left(t\right)+\mu_{5}\left(t\right)\right)\right]^{\frac{1}{2}}\end{split}
# Experimental Demonstration of Terahertz-Wave Signal Generation for 6G Communication Systems
## 2 Simulation Model for THz Generation
We analyzed the EVM in relation to the optical received power for the generated optical vector THz signal, with the findings presented in Figure 7b. The EVM decreases with increasing optical received power. At the EVM threshold value of \(5.6\%\), as noted in [62], the power level is approximately \(-13\) dBm. Additionally, the constellation diagram is distinctly separated and clearly defined. In the subsequent analysis, we examined the bit error rate (BER) under two scenarios: back-to-back (BtB) transmission and transmission over a 15 km SMF, as illustrated in Figure 8. The results indicate that the BER decreases as the optical received power increases for both the BtB and SMF cases. At the HD-FEC threshold value of \(3.8\times 10^{-3}\), the power for BtB transmission is \(-16.7\) dBm, while for the 15 km SMF transmission, it is \(-15.2\) dBm. This results in a power penalty of \(1.5\) dB between the two scenarios, attributed to fiber dispersion and phase fluctuations. Nevertheless, this difference can be considered negligible, and we can treat the SMF at this length as effectively transparent, as will be demonstrated in the experimental results. Upon analyzing Figures 7 and 8 alongside the findings presented in reference [58], it becomes evident that there has been a notable enhancement in power performance. This comparison not only highlights the advancements achieved but also underscores the effectiveness of the methodologies employed in the current study. The improvements observed suggest a shift in power efficiency, indicating that the new approaches may  
Figure 8: BER curves for BtB transmission and SMF 15 km. Figure 7: Performance curves for generated vector THz-wave signal for (**a**) BER and (**b**) EVM versus optical received power. lead to more robust applications in the field. As we delve deeper into the data, it is clear that these enhancements could have far-reaching implications for future research and practical implementations.
## 3 Materials and Methods
### Analysis of Samples  
The materials were analyzed with the aim of determining the basic physical and chemical parameters, e.g., the total content of carbon, ash, nitrogen, hydrogen, volatile substances and calorific value. The thermogravimetric method, with use of TGA 701 apparatus from LECO (LECO Corporation, Saint Joseph, MI, USA), was used to analyze the contents of ash and volatile substances in the samples. The TrueSpec CHN analyzer from LECO (LECO Corporation, Saint Joseph, MI, USA) was used to test the contents of the total carbon, hydrogen and nitrogen. The AC500 calorimeter from LECO (LECO Corporation, Saint Joseph, MI, USA) was used to determine the calorific value of the materials analyzed. Analysis of the content of carbon monoxide (CO), nitrogen oxides (NOx) and sulfur oxide (SO\({}_{2}\)) was carried out using an ULTRAMAT 23 gas analyzer (SIEMENS AG, Munich, Germany). An algorithm that takes into account the density of each gas was used to calculate the content of the individual gases. Table 1 presents an example of the calculations for CO.  
\begin{table}
\begin{tabular}{c c} \hline
**Calculated Parameters** & **Method of Calculation** \\ \hline The amount of carbon monoxide (CO) & SCO = 0.5 \(\times\) (1X\({}_{n-1}-X_{n}\)) \(\times\) (t\({}_{n-1}-t_{n}\)) \\ Amount of gas mixture taken & S\({}_{\text{gas}}\) = t\(\times\) 100 \\ Percentage of carbon dioxide (CO) in the analyzed gas mixture & \(\%\)CO = SCO/S\({}_{\text{gas}}\) \\ Amount of gas mixture taken during the test & AL = t\(\times\) 0.025 [L/s] \\ Amount of carbon monoxide (CO) taken during the test & ACO = AL \(\times\) \(\%\)CO \\ Carbon monoxide (CO) density at room temperature (20 \({}^{\circ}\)C) [mg L\({}^{-1}\)] & 1250 \\ Amount of carbon monoxide (CO) taken during measurement [mg] & ACO \(\times\) 1250 \\ \hline \end{tabular}
\end{table}
Table 1: Algorithm for calculating gas quantities (example for CO).  
Figure 1: Cakes of fennel flower (**a**), rapeseed (**b**), flax (**c**), evening primrose (**d**), milk thistle (**e**) and hemp (**f**).  
where  
SCO--the calculated amount of CO;  
S\({}_{\rm gases}\)--the amount of gas mixture taken;  
X\({}_{n-1}-X_{n}\)--CO concentration results recorded by the analyzer during the intervals;  
t\({}_{n-1}-\) t\({}_{n}\)--measured time intervals;  
t\({}_{\rm c}\times 100\)--100-percentage sum of absorbed gases;  
AL.--the amount of gas mixture taken during the test;  
ACO--the amount of CO taken during the test;  
0.5--the complement of the curve integrating the measurement result;  
0.025--the amount of air suctioned per unit time [L/s].  
Table 2 summarizes the density parameters of the analyzed gases, which were used in the calculations.  
Samples of biomass and biochars were subjected to laboratory analyses using current analytical standards (Table 3).
## 4 Results and Discussion
### Discussion  
Computer-mediated methods of delivering detailed assessment feedback have strong potential as an efficient way of delivering quality feedback when it is structured according to best practice and which contains orders of magnitude and more information than a conventional marker would have time to write.  
We might therefore characterize the process of computer-mediated feedback examined in this study as follows:  
* Has the virtue of simplicity and efficiency, requiring fewer marking resources, which is an advantage, particularly in larger courses, allowing for courses to be scaled-up without loss of quality if not an improvement in quality;
* Can deliver quality feedback that is informed by best practice;
* Can deliver a large amount of feedback with no extra effort;
* The widespread availability of LMS technology and the simplicity of the process means that the practice could be generalized across disciplines; and
* The process produces measurable improvements in both student satisfaction and learning, the combination of which is likely to improve student engagement and retention which is a strategic imperative for any institution.  
Scaled-up, computer-mediated methods such as this have potential to address equity of access to education in developing parts of the world. Courses can be made available to people in disadvantaged areas provided they have a computer and an internet connection. This may be a great benefit to women, given the lower rates of participation in technology relative to men [23].  
The practice of delivering standardized feedback via the LMS does not please everyone, specifically those who expect personally addressed feedback. In this study, less than 20% of respondents expressed dissatisfaction with the method. A possible remedy is for the marker to append a voice-recorded message in addition to the written feedback, or to offer one-to-one consultations, where practical.