esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data and Data Processing  
#### 2.2.1 EC Flux Tower Data  
The CO\({}_{2}\) eddy flux tower site data used in this study were accessible through the AmeriFlux network [34], which consists of over 150 sites making micrometeorological, meteorological and biological measurements in different biomes of North and South America [17]. In this study, we used EC flux tower data with Level 4 (L4) processing, (since it provided GPPec values compiled for half hourly and weekly (eight-day) intervals for each site. These data also included meteorological and soil data measured at the tower. A more extensive description of these variables and processing levels can be checked on the Ameriflux website [34].  
GPPec data were provided as original GPP (GPP_or) and/or standardised GPP (GPP_st). GPP_st was derived from standardised NEE and it was used in all cases for this work, unless it was not available (_i.e._, USFPe, USAUD), since it allowed for a more accurate comparison across sites [19]. Since gaps in NEE acquisition are unavoidable, artificial neural network (ANN) gap-filled GPP data were used, as recommended by [15, 18, 35]. However, this is not a critical decision as long as the same method was chosen for all sites.  
While examining the eight-day L4 flux tower data, it was observed that some of the GPP values were negative, especially for the water-limited sites (_i.e._, USAUD and USVAR). Negative GPP data can be the result of flaws in the standardised partitioning of the net CO\({}_{2}\) (NEE) when the true GPP values are close to zero [30] and it is more likely to happen in water deficient areas [15] (_i.e._,  
Figure 1: Location of the six sites used in this study: USAUD, USFPe, USVAR, USSO4, USKS2 and USME3.  
USVAR). Therefore, the half-hourly L4 data were reviewed, since the NEE partitioning was performed at that time interval. The separation of NEE into GPP and Re is a complex and controversial task [36, 37], which has been the objective of other comprehensive studies [15] and is not the goal of this work. As expected, the half-hourly data contained negative GPP values as well (especially during night time periods), which led to negative aggregated eight-day GPP data or GPP values that were lower than the actual ones. [PERSON], _et al._[6] and [PERSON], _et al._[38] also reported negative night time GPP values and noted that both negative GPP values and non-zero GPP values at night are impossible in the majority of plants, and therefore, the values were interpreted as errors and were made to be zero. We followed this methodology and we made the negative half-hourly GPP values zero. However, when the L4 eight-day data showed GPP values smaller than -1 g C-day-1, those periods were excluded from the analysis (for both the weekly and the half-hourly data) because those values were too negative to be corrected to 0, and therefore, showed an anomaly in the data. This was the case for USAUD and USFPe, for which 18.8% and 27.2% of the original data were not considered for the analysis, respectively.
### Delay of Transmission  
The average data transmission delay with respect to different void region radii is shown in Figure 13. Owing to the data fusion delay in clusters, GTRF spends more time than the hop-by-hop routing protocol. However, GTRF still has the merit that fewer nodes will appear within a path, which reduces the possibility of encountering a void region when forwarding packets. Therefore, the probability of such a delay is small. Figure 13 indicates that there is no direct relationship between the size of the void region and the network delay in GTRF. For transmitting 1000, 2000, 5000 and 10,000 packets, GTRF presents nearly the same transmission delay tendency. Although the delay of GTRF is higher than FTSPEED and DMRF, and even in some cases, it is also higher than the other real-time routing protocols (e.g., SPEED, SPEED-T, SPEED-S, MMSPEED), GTRF still can achieve real-time transmission.  
Figure 12: Number of control packets after transmitting (**a**) 1000; (**b**) 2000; (**c**) 5000 and (**d**) 10,000 packets under different routing protocols.
## 2 Mathematical Foundation of Generalized Algorithm
### Higher Order Modes  
The model from [20] assumes that only dominant mode \(TE_{10}\) propagates in the enclosure. However, if the electronic equipment enclosure is electrically small or is affected by broadband electromagnetic interference, higher-order modes \(TE_{mn}\) or \(TM_{mn}\) can be excited in the enclosure. In [23], a modification of the equivalent circuit model was proposed that takes into account higher-order modes in SE calculations. As in [20], the SE is determined from the current \(i_{p}\) or voltage \(V_{p}\) values in the equivalent circuit, but these values are calculated iteratively. First, the characteristic impedance \(Z_{g\,mn}\) and the propagation constant \(k_{g\,mn}\) are calculated, depending on the non-negative integers \(m\) and \(n\) that determine the numbers of modes excited in the enclosure. For \(TE_{mn}\) modes, \(Z_{g\,mn}\) and \(k_{g\,mn}\) are calculated as:  
\[Z_{g\,mn}=Z_{0}/\sqrt{1-\left(\frac{\lambda m}{2a}\right)^{2}-\left(\frac{ \lambda n}{2b}\right)^{2}}, \tag{24}\]  
\[k_{g\,mn}=k_{0}\sqrt{1-\left(\frac{\lambda m}{2a}\right)^{2}-\left(\frac{ \lambda n}{2b}\right)^{2}}. \tag{25}\]  
For \(TM_{mn}\) modes, the \(k_{g\,mn}\) value is also calculated using (21), and \(Z_{g\,m}\) is defined as:  
\[Z_{g\,mn}=Z_{0}\sqrt{1-\left(\frac{\lambda m}{2a}\right)^{2}-\left(\frac{ \lambda n}{2b}\right)^{2}}. \tag{26}\]  
At the fixed values of \(Z_{g\,mn}\) and \(k_{g\,mn}\), the transformation of the equivalent circuit is performed using expressions (4)-(8). Using (9), the voltage \(V_{p\,mn}\) for the current mode type and number is calculated and written to the counter. Then, the \(m\) and \(n\) indexes are changed, and the calculation is repeated again. As a result, based on the principle of superposition,  
Figure 5: The aperture populated with wire grid (**a**) and geometry of this wire grid (**b**).  
voltages \(V_{p\,mn}^{TE}\) and \(V_{p\,mn}^{TM}\) are calculated, correspondingly, to \(TE_{mn}\) and \(TM_{mn}\) modes. The \(V_{p}\) value used to calculate the SE is then defined as:  
\[V_{p}=\sqrt{\left(V_{p\,mn}^{TE}\right)^{2}+\left(V_{p\,mn}^{TM}\right)^{2}}. \tag{27}\]  
Note that another approach for taking into account higher-order modes was proposed in [24]. However, the procedure of equivalent circuit transformation used in this approach is very different from the model [20], which makes it difficult to use this procedure in the generalized algorithm of SE evaluation.
# Superpixel Segmentation Using Weighted Coplanar Feature Clustering on RGBD Images
## 5 Experimental Results and Analysis
### Boundary Recall  
Boundary recall is defined as the part of ground truth edges within a certain distance (\(d\)) from a superpixel boundary. Here, the value of \(d\) is set to 2, which means a two-pixel distance error. Assuming \(I_{gt}\) is a ground truth boundary of the image and \(I_{b}\) is the boundary calculated by the superpixel segmentation method, \(TP(I_{b},I_{gt})\), \(FN(I_{b},I_{gt})\) and \(BR(I_{b},I_{gt})\) represent true positives, false negatives and boundary recall, respectively:  
\[BR=\frac{TP(I_{b},I_{gt})}{TP(I_{b},I_{gt})+FN(I_{b},I_{gt})}, \tag{9}\]  
where \(TP\) is defined as the number of boundary pixels in \(I_{gt}\) with at least one boundary pixel in \(I_{b}\) in range \(d\). In contrast, the number of boundary pixels in \(I_{gt}\) is denoted by \(FN\) so that there is no boundary pixel found in \(I_{b}\) in range \(d\). The boundary recall is expected to be high in order to make superpixels respect the boundaries in an image.  
To fully access the performance of superpixel methods, we calculated the \(BR\) on the whole RGBD image as opposed to only in regions with depth values in the raw RGBD image, which was done  
Figure 4: Visual comparison of superpixels produced by simple linear iterative clustering (SLIC), depth-adaptive superpixels (DASP), voxel cloud connectivity cegmentation (VCCS) and our WCFC method. The average superpixel size is approximately 500 pixels in the upper left of each image and approximately 300 in the lower right.  
in [28; 29]. Therefore, the results given in this section are a bit different from those of existing works. Since DASP and VCCS obtained undesired results on the inpainted RGBD images, we used SLIC on the regions without depth values instead, following the instructions in [28]. The results are shown in Figure 5, and it is clear that WCFC performed best within the five types of superpixel algorithms and the content-adaptive weight improved the \(BR\) of our WCFC.
## 3 Research Methods
### Correlation Analysis
#### 3.4.2 Net Correlation Analysis  
Net correlation analysis refers to the examination of the correlation between two variables while controlling for the influence of other variables that may affect both. The resulting correlation coefficient is termed the net correlation coefficient [21]. In this study, we focus on analyzing the correlation between landslide deformation and reservoir water level. However, since rainfall during the period also affects landslide deformation, analyzing only the influence of reservoir water level on landslide deformation may not accurately reflect their complex relationship.  
To mitigate the influence of rainfall on the relationship between water level and deformation, we control for the rainfall variable. The formula is as follows [22]:  
\[r_{12-3}=\frac{r_{12}-r_{13}r_{23}}{\sqrt{(1-r_{13}^{2})(1-r_{23}^{2})}} \tag{2}\]  
In Equation (2), \(r_{12-3}\) denotes the net correlation coefficient between variables \(1\) and \(2\) when variable \(3\) is controlled. \(r_{12}\) represents the Pearson correlation coefficient between variables \(1\) and \(2\). \(r_{13}\) stands for the Pearson correlation coefficient between variables \(1\) and \(3\). \(r_{23}\) represents the Pearson correlation coefficient between variables \(2\) and \(3\). The net correlation coefficient \(r_{12-3}\) aligns in level of relevance and direction with the Pearson correlation coefficient \(r\).
## 4 Results and Discussions
### Effect of Acid-Activation
However, the strong acid treatment that produced S\({}_{3}\), caused large increase of pore volume in the range of micro pore and meso pore diameter and consequently an increase in average pore \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Samples** & **Total Pore Volume (10\({}^{-2}\) cm\({}^{3}\)/g)** & **Average Pore Diameter (Å)** & **BET Surface Area. S\({}_{\rm BET}\) (m\({}^{2}\)/g)** \\ \hline
**S\({}_{0}\)** & 13.74 & 79.56 & 69.00 \\
**S\({}_{1}\)** & 17.20 & 72.92 & 94.36 \\
**S\({}_{2}\)** & 22.26 & 60.76 & 146.50 \\
**S\({}_{3}\)** & 45.58 & 98.9 & 184.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Textural properties of raw material (S\({}_{0}\)) and acid-activated clay samples (S\({}_{1}\), S\({}_{2}\), S\({}_{3}\)). Figure 9: N\({}_{2}\) adsorption isotherms of the raw material (**S\({}_{0}\)**) and activated clay samples (**S\({}_{1}\)**, **S\({}_{2}\)**, **S\({}_{3}\)**) with \(\pm\)5% error bars in volume. diameter. This suggests a considerable structural changes and decomposition occurred in this sample. A similar result was observed by [PERSON] and [PERSON] [40]. The change in mean pore diameter with the acid treatment is also confirmed by the mesopore size distribution curve calculated by BJH equation [41]. The pore size distribution for both raw material and acid-activated clays (S\({}_{1}\), S\({}_{2}\) and S\({}_{3}\)) is presented in Figure 10.  
From pore size analysis, it was found that the most frequent pore size of the raw material (S\({}_{0}\)) is 3.7 nm, the same result is found for samples S\({}_{1}\) and S\({}_{2}\). However, in higher conditions, the activated clay mineral (S\({}_{3}\)) has mesopores with the most frequent size of 6 nm. The increase of peak amplitude and width of the sample S\({}_{3}\) confirm that the pore volume increases with acid-activation. The large increase in pore volume and the enlargement of the pore size distribution, observed during the strong acid treatment, suggest that significant structural changes and partial destruction may occur in this sample. Similar tendency was reported by [PERSON] [9]. These changes in pore structure may result from the removal of exchangeable cations and impurities from the clay lattice following the H\({}_{2}\)SO\({}_{4}\) attack.
## 4 Results and Discussion
### Location-Allocation Results of Fixed Emergency Shelters

Table 2: The number and coverage rate of the fixed emergency shelters in the Jianchuan county seat. There is no seismic fault zone inside, nor any large water areas. Through the analysis of the town's major hazard source buffer, there is no emergency shelter located in the radiation range of the city's major hazard source at a distance of 1 km. In the eight exiting fixed emergency shelters, Jingfeng Park is unsuitable as a fixed emergency shelter due to the limitation of its actual effective area. Therefore, its location should be adjusted. Since Jingfeng Park, Jinhua middle school, and Jianchuan high school are nearby, and the scope of the demand points which they cover is close, thus, the authors attempted to merge Jianchuan middle school and Jinhua high school as a new fixed emergency shelter. As a result, there are seven fixed emergency shelters, and the results of the model are displayed in Figure 3, with a coverage rate of 81.72%, satisfying the requirements.
## 3 Results and Discussion
### Molecular Docking  
The 43 selected compounds were docked into the binding site of the TMV-CP (PDB ID: 2 OM3) to assess their abilities to inhibit the disease caused by the virus. The results obtained are summarized in Table 4.  
The molecular docking score of the 43 compounds varied from \(-\)5.29 to \(-\)7.93 kcal/mol, and the RMSD ranged between 0.71 and 2.83 A. The ligand binds best with a specific receptor when RMSD is close to 2 A with an energy score that \(\leq\)\(-\)7 kcal/mol [42; 43]. These two values were used as criteria to validate the molecular docking results. Table 5 shows the results obtained for only the compounds that gave valid molecular docking scores.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Compd**} & \multirow{2}{*}{
\begin{tabular}{c} **Score** \\ **(kcal/mol)** \\ \end{tabular} } & \multirow{2}{*}{**RMSD (Å)**} & \multirow{2}{*}{**Compd**} & \multirow{2}{*}{**Score** (kcal/mol  
+++ ==WARNING: Truncated because of repetitions==
)**} & \multirow{2}{*}{**RMSD (Å)**} \\ \cline{5-5}  & & & & & & \\ \hline  
++  
Compounds, **1**, **21**, and **22** showed the highest docking scores of \(-6.98\), \(-7.93\), and \(-7.25\) kcal/mol, respectively (i.e., they have the highest inhibitory potential). Compound **21** docked with a binding affinity of \(-44.72\) kcal/mol, which is the highest compared with the others and interacted with Arg90 and Arg92. Compound **22** docked with a binding affinity of \(-39.90\) kcal/mol and interacted with Arg90 and Arg92. Compound **1** docked with a binding affinity of \(-37.59\) kcal/mol and interacted with Thr42, Gln38, and Arg90. The docking binding affinities were lower for the other compounds.  
Compounds **20**-**22** have similar structures since they contain esters and long-chain alkyl groups but have different substituents on benzyl rings. However, they had different binding affinities. Compound **20** gave the lowest binding affinity among the three compounds. It seems that the effect of the donor groups NMe\({}_{2}\) and OCH\({}_{3}\), attached at the _para_ position of the benzyl moiety within the skeleton of the molecules, caused **21** and **22** to interact more significantly. Figure 1 represents the interactions of compounds **1** and **20**-**22** with 2 OM3.  
Figure 1: Binding affinity of compounds **1** and **20**–**22** with 2 OM3.
## 1 Introduction
We show that the proposed scheme has low complexity, and one can obtain consistent output irrespective of the order of the data to be combined, which is beneficial in the autonomous vehicle environment. In other words, the method is insensitive to external factors that may affect data arrival time such as channel states or vehicle speed. 2. _An integrated collision warning protocol for autonomous vehicles._ We propose a cooperative collision warning protocol integrating the proposed data fusion scheme. For real-time operation, the system is composed of technologies in various fields that can guarantee low latency and high reliability. By experiments, we show that the method takes only a few milliseconds to return credible detection of nearby objects. 3. _Experiments with real-world data._ By simulation, we show that the proposed scheme reduces false negative errors as compared to the existing methods. In addition, we conduct real-world experiments and show that the proposed system not only prevents collision from blind spots, but also compensates for inaccurate sensing information. A comprehensive evaluation of the proposed data fusion shows that our scheme reduces fault diagnosis compared to the baseline methods. In addition, the proposed scheme has low complexity, which enables combining gathered data in real-time. Next, experiment results reveal that the proposed collision warning system with data fusion can successfully detect and avoid imminent collisions. The rest of this paper is organized as follows. In Section 2, we present related works. In Section 3, we introduce background evidence theory and propose the data fusion schemes based on a new metric for evidence distance. The collision warning system that involves the proposed data fusion processes is proposed in Section 4. Section 5 shows the performance evaluation and experiment results with real-world data, and Section 6 concludes this paper.
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using On/Off-Policy
#### 3.3.3 Localization  
In [86], the authors present a method to use LiDAR technology to determine the precise location of high-speed autonomous racing cars. The framework uses multiple independent estimators that analyze data from various sensors simultaneously. It is based on the Adaptive Monte Carlo Localization (AMCL) algorithm, which uses LiDAR data to pinpoint the vehicle's location, as shown in Figure 17. The authors aimed to improve computing efficiency in scenarios with limited distinct features, unclear motion patterns, and quick detection of incorrect locations. The authors boosted the precision and reliability of the vehicle's positioning by merging data from multiple sources and systematically integrating them to improve location estimates. The work also used a Kalman filter that provided more significance to auxiliary data collected through LIDAR estimations.  
The authors in [86] improved the AMCL algorithm by focusing on how well it extracts LiDAR data and adding an assumption for angular noise that changes with velocity. An analytical assessment technique was used to discover localization issues and provide comments. The AMCL algorithm was modified to reduce computational time, and a \"corridor optimized\" beam extraction method was used for racetrack construction. An ROS enabled data collection and processing from several sensors, providing standardized  
Figure 17: Sensor fusion for RL based autonomous driving. For more information, please refer to Table 4.  
interfaces for LiDAR, GPS, IMU, and odometry sensors. The authors underscore the reliability of the method in high-speed scenarios and substantiated its effectiveness using empirical data from an autonomous high-speed racing vehicle.  
The authors have made substantial advancements in autonomous vehicle localization by tackling challenges related to high-speed driving, especially in racing environments. They provide a methodology that addresses specific requirements and incorporate a system-wide perspective for durability and dependability. Their work has implications for various sectors, such as emergency response and rapid transit systems. The research has advanced real-time localization algorithms and sensor fusion techniques, promoting the safety and efficiency of high-speed autonomous driving. By tackling these specific challenges, they have demonstrated the ability of autonomous vehicles to operate effectively in dynamic and unpredictable environments, which leaves this work open to implementation for other applications such as logistics.
## 4 Experimental Analysis
### Summary and Analysis  
The experimental results presented in this section demonstrate the impressive capability of TCReC in reconstructing network traffic characteristics, surpassing traditional data completion methods and other machine learning and deep learning-based approaches. TCReC exhibits robustness in supporting various detection tasks in the presence of packet loss. Notably, the reconstruction ability of TCReC remains stable across different detection tasks, although its effectiveness may be influenced by the performance of the detection classifier. In our experiments, we utilized CNN and LSTM as the underlying detection classifiers, achieving favorable outcomes. This provides assurance that TCReC can deliver high-quality input data in scenarios with packet loss when integrated with other advanced detection models.
# On Self-Interference Cancellation and Non-Idealities Suppression in Full-Duplex Radio Transceivers
## 2 Proposed System Model and Techniques
### Link Budget Analysis  
Link budget analysis describes the details of signal components at every stage of Tx and Rx. These details include noise figure, gain, SNR, and IIP3, as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Modulation Scheme & 16 QAM \\ \hline Number of Sub-carriers & 64 \\ \hline Number of Data sub-carriers & 48 \\ \hline Symbol Time & 3.2 \(\upmu\)s \\ \hline Guard Interval or CP Duration & 25\% of symbol length \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test signal “OFDM” parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component** & **Gain** & **IIP2** & **IIP3** & **NF** \\  & **(dB)** & **(dBm)** & **(dBm)** & **(dB)** \\ \hline IQ Mixer (Tx and Rx) & 6 & 50 & 15 & 4 \\ \hline PA & 27 & - & 20 & 5 \\ \hline LNA & 25 & - & 5 & 4.1 \\ \hline VGA & 1–51 & 50 & 20 & 4 \\ \hline IQ Mixer IRR (dB) & 13 & - & - & - \\ (Tx and Rx) & 6 & - & - & - \\ \hline \multirow{3}{*}{ADC} & Bits & 12 & - & - \\ \cline{2-4}  & P-P voltage range & 4.5 V & - & - \\ \cline{2-4}  & PAPR & 10 dB & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Non-linear and linear parameters of transmitter and receiver chain.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Modulation Scheme & 16 QAM \\ \hline Number of Sub-carriers & 64 \\ \hline Number of Data sub-carriers & 48 \\ \hline Symbol Time & 3.2 \(\upmu\)s \\ \hline Guard Interval or CP Duration & 25\% of symbol length \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test signal “OFDM” parameters.