esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis
### Spatial Correlation Analysis of Carbon Emissions  
The estimated results of Table 5 are consistent with those of previous studies, indicating that there are spatial effects on carbon emissions in cities in the Yangtze River Delta [40], but the difference is that the carbon emissions in the Yangtze River Delta show significant positive spatial correlation under the economic distance spatial weight matrix. Under the adjacent spatial weight matrix, the carbon emissions of the Yangtze River Delta show negative spatial correlation, but at the 10% significance level, this negative spatial correlation is not significant. In regard to the economic distance and space weight, at the 5% significance level there is a positive spatial correlation between carbon emissions in  
Figure 3: Scale variance decomposition of different scales of the Yangtze River Delta in 2008–2015.  
areas of similar economic development. From this, it can be judged that the geographical proximity is not the main reason for the spatial correlation of carbon emissions in the Yangtze River Delta. This may be because different city-level units use administrative orders to lay out the industry. At junctions between cities, there is often a county with high carbon emissions holding another county with low carbon emissions. This phenomenon is particularly common in the Jiangsu province. For the purpose of protecting the environment of this city, some cities have placed high-energy consumption and high-pollution enterprises in county areas where the city's winter winds travel towards, and for neighboring cities, the county in this position is the central city. The administrative units do not deploy high-pollution and high-energy-consuming enterprises in the upward direction, which results in a negative spatial correlation of carbon emissions in geographically adjacent areas. The reason why this negative spatial correlation is not significant may be that the administrative boundaries of different urban areas have became blurred at the county scale.  
The carbon emissions in the Yangtze River Delta show a significant positive spatial correlation under the economic distance spatial weight matrix. This shows that the areas with similar economic development levels have similar carbon emissions. The reason for this is that the areas with similar levels of economic development have mutual learning and mutual influence during the process of urbanization, industrial planning, and economic development planning.
# Free Space Optics Communication for Ultra-High-Speed Train Running in Evacuated Tube
## 2 System Model
### The Received Power  
The received power [20] at the receiver can be stated as follows for standard ground-to-train FSO communications:  
\[P_{rx}=P_{tx}\frac{D^{2}}{\theta_{div}\,^{2L^{2}}}10^{-\gamma L/10}\eta_{tx} \eta_{rx} \tag{4}\]  
where \(P_{tx}\) represents the transmitted power, \(\theta_{div}\) denotes transmitter divergence angle, \(D\) represents receiver diameter, \(L\) indicates communications distance, \(\gamma\) is the atmospheric attenuation coefficient in dB/km, and \(\eta_{rx}\) and \(\eta_{tx}\) denote receiver and transmitter efficiency, respectively. Since \(P_{tx}\) is 100 mW for 1 cm\({}^{2}\) and \(D\) is 11 cm [13] (e.g., see Table 1), the beam's divergence angle (here, \(6.944\times 10^{-5}\) is the smallest angle) is defined. The communication distance between the transmitter and receiver is determined. Therefore, the received power can be estimated with the attenuation effect.  
Due to the unique design for the ETT built for air-free evacuated tubes (to mitigate the noise and the resistance of the air), the attenuation from the weather effect is zero. Therefore, instead of \(\gamma\), we can write zero to represent it.  
Or, simply Equation (5) (Friis formula) can be used instead of (4) [21]:  
\[P_{rx}=P_{tx}G_{tx}G_{rx}\left(\frac{\lambda}{4\pi R}\right)^{2}L_{geo}L_{tx} L_{rx}\eta_{tx}\eta_{rx} \tag{5}\]  
where \(G_{tx},G_{rx}\) correspond to the transmit and receive antenna gains, \(R\) is the communication distance between the transmitter and the receiver, \(L_{geo}\) is the geometrical loss, \(L_{tx},L_{rx}\) are transmitter pointing loss and receiver indicating loss, respectively, and \(\eta_{tx},\eta_{rx}\) are transmitter and receiver optical efficiency, respectively. Equation (6) gives an approximation of the transmitter antenna gain for a Gaussian beam:  
\[G_{tx}=\frac{32}{\theta^{2}} \tag{6}\]  
The receiver antenna gain is given by [21; 22]:  
\[G_{rx}=\left(\frac{\pi D_{rx}}{\lambda}\right)^{2} \tag{7}\]  
In addition, \(L_{geo}\) is given by [23]:  
\[L_{geo}=\left(\frac{D_{rx}}{D_{tx}+\theta R}\right)^{2} \tag{8}\]  
\(L_{tx}\) and \(L_{rx}\) in (4) are the transmitter and receiver pointing loss [24], respectively, which are given by:  
\[L_{tx}=e^{-G_{tx}\gamma^{2}} \tag{9}\]  
\[L_{rx}=e^{-G_{rx}\gamma^{2}} \tag{10}\]  
where \(\gamma\) and \(\zeta\) indicate the radial aiming errors of the transmitter and receiver, respectively. Moreover, such a model should indicate the loss attenuation using Equation (11). In this paper's case, because of the evacuated tube, there is no attenuation loss from the air fog, etc.  
\[L_{a}=\frac{17}{V}\left(\frac{\lambda}{0.55\mu m}\right)^{-q(\lambda)} \tag{11}\]In (11), \(L_{a}\) is in dB/km, \(V\) is the weather visibility in km, and \(q\) is the size distribution of the scattering particles, \(q\) values are given in [25].
# Decentralized Multi-Agent Control of a Manipulator in Continuous Task Learning
## 4 Implementation of Proposed Approach
### Action Space  
The action space consists of an 8-dimensional vector, which represents the 7 joints of the panda robot and the gripper. The last dimension of the action space represents the actuation control of the gripper and is mirrored to have a symmetrical action for the fingers. The action space is split into two equal parts. One part of the manipulator corresponds to the motors of the first 4 joints, while the second part corresponds to the last 3 joints and the gripper's actuation. Dividing the action space in such a way allows for the decentralization of the system and shows the modularity of the robot. Joint velocities coming from the agents are fed into a controller that converts the desired joint velocities into joint torques. The robot is considered to be equipped with torque control compensating for the manipulator dynamics [42]:  
\[\mathbf{\tau}=\textbf{B}(\textbf{q})\mathbf{\tau}_{learn}+\mathbf{\tau}_{f}(\dot{\textbf{q }})+\textbf{C}(\textbf{q},\dot{\textbf{q}})+\textbf{g}(\textbf{q}). \tag{1}\]  
In this equation, \(\textbf{B}(\textbf{q})\) represents the robot inertia matrix, \(\textbf{C}(\textbf{q},\dot{\textbf{q}})\) is the robot Coriolis vector, \(\textbf{g}(\textbf{q})\) is the robot gravitational vector, \(\mathbf{\tau}_{f}(\dot{\textbf{q}})\) is the robot joint friction vector, \(\textbf{q}\) is the current joint position vector, and \(\mathbf{\tau}\) is the control torque vector. \(\mathbf{\tau}_{learn}\) is the learned task-related control torque vector mapping the reference joint velocities to joint torques for the target task execution.  
\[\mathbf{\tau}_{learn}=\textbf{k}_{v}(\dot{\textbf{q}}^{d}-\dot{\textbf{q}}), \tag{2}\]  
where \(\dot{\textbf{q}}^{d}\) and \(\dot{\textbf{q}}\) are the reference (i.e., the objective of the learning) and current joint velocities respectively, and:  
\[\textbf{k}_{v}=diag(8,7,6,4,2,0.5,0.1). \tag{3}\]  
\(\textbf{k}_{v}\) is a diagonal matrix of fixed proportional gains for joint trajectory-tracking purposes. These control gains need to be tuned to allow the robot to track the reference joint velocities \(\dot{\textbf{q}}\). The values that are selected are based on the variable impedance control [43], which results in accurate trajectory tracking performance. If needed, such control gains can be either learned or experimentally tuned to improve trajectory tracking performance.
## 6 Performance Evaluation
### Performance of Data Distribution Storage  
The data distribution storage performance compared the basic EC-based HDFS with the I/O buffering technique and I/O combining technique-applied EC HDFS-BC proposed in this study.  
Figure 20 shows the disk throughput when storing 100 GB sample data to each distribution file system after creating the sample data in the EC-based HDFS and EC HDFS-BC systems. The EC-based HDFS showed a slight performance increase though, but no significant change was shown throughout the experiment, whereas the EC HDFS-BC system exhibited a high write throughput as it closed to the file storage completion, which was improved about 2.5-fold compared with that of the EC-based HDFS.  
Figure 21 shows a performance comparison when storing sample data with different sizes in the EC HDFS and EC HDFS-BC systems. No significant difference was exhibited when storing 10 GB data, and when storing 50 GB data, the EC HDFS-BC system stored data about 1.3 times faster than the existing EC HDFS. When storing 100 GB data, the storage time of EC HDFS-BC was about twofold faster. The above experiment results show that if larger-size data are stored, more stark difference in storage time would be expected.  
Figure 20: Comparison between EC-based HDFS and improvement-applied performances.
## 5 Conclusions  
In this paper, the robotic polishing process of marble with complex curved surfaces based on SG flexible tools was investigated and polishing experiments on Hanbaiyu marble workpieces were conducted. The main conclusions are as follows:  
1. In this paper, SG flexible tools were manufactured based on SG flexible polishing film and flexible fiber. When the diamond abrasive grains are in contact with the marble, the SG flexible polishing film yields at a micro level and the flexible fiber layer yields at a macro level to achieve double yielding at both the micro and macro levels so that the SG flexible tools can better fit to the complex curved surface.
2. The relationship between surfaces of different curvatures and polishing forces was built by simulating the contact of the SG flexible tool. Based on the [PERSON] material removal theory, the complex contact force distribution between the SG flexible tool and the marble, as well as the constant-pressure material removal contour, were simulated to achieve constant-pressure polishing of surfaces of different curvature, which ensures consistency of the polished surfaces.
3. When SG flexible tools are used to grind and polish marble surfaces of different curvature, the optimum polishing trajectory space and the curvature of curved marble can be well matched to a curvilinear model, which can be used to guide the actual machining.
4. The polishing of the Hanbaiyu marble workpieces demonstrates that by using the SG flexible tool developed in this study in robotic polishing of marble with complex curved surfaces, we can obtain a polished marble with both good surface quality and high consistency, which effectively improves the quality of the products and further increases their added value. This also validates the high consistency of the surfaces after polishing with the optimized trajectory space.  
In summary, this paper presents a study on the robotic polishing process of complex curved marble surfaces based on the SG flexible tool, providing new ideas and methods for the intelligent development of the stone processing industry. Future work will continue to delve into the issues of errors and uncertainties in the polishing process, further refining polishing methods and techniques to make greater contributions to the sustainable development of the stone processing industry.  
Conceptualization, S.H. and J.L.; Methodology, F.Y. and J.L.; Validation, S.H. and J.L.; Formal analysis, J.H.; Writing--original draft, Y.G.; Writing--review & editing, S.H., Y.G. and J.H.; Visualization, Y.G.; Project administration, S.H. and F.Y.; Funding acquisition, S.H. and F.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors appreciate the financial support from Fujian Science and Technology Project (Grant No. 2022H0018, 2022H6029).  
The authors declare no conflict of interest.
## 2 System Model  
The coverage density in the room affects the connectivity of signal transmission and also makes the window of RIS components small. As described in Section 1, to maximize the spectral efficiency of the system, RIS reflectance and hybrid beamforming design are particularly important. But, due to the non-convexity of this problem, it can be decomposed into two independent optimization sub-problems. The RIS-assisted multiuser millimeter-wave massive MIMO/FSO UAV communication system is shown in Figure 1. The source node (S) communicates with the destination node (D) by decoding forwarding (DF) and relay node (R). The S-R link is equipped with a single antenna, and the R-D link is a millimeter three-wave massive MIMO system model. Node R has optical and RF signal processing capabilities. An incoherent intensity modulation and direct detection (IM/DD) receiver is employed at node R. The power divider separates the electrical signal, which is converted from the incoming optical signal from the alternating current (AC) and coupling (DC) components. An unsolicited DC component, usually filtered out at the receiver, is applied to an energy harvesting unit that provides the collected power to the RF transmitter. The AC component carrying the information is decoded by the decoder circuit. The decoding information is forwarded to the RF transmitter after demodulation according to the RF modulation scheme.
# Trajectory Planning of Shape-Following Laser Cleaning Robot for the Aircraft Radar Radome Coating
## 2 Materials and Methods
### Measurement and Preprocessing of Point Cloud Data for Radar Dome Surfaces
#### 2.1.3 Point Cloud Data Alignment
\[m=|z_{k1}|-|z_{k2}| \tag{10}\]  
Figure 5: Alignment of radar hood point cloud coordinates (\(Z\)-axis rendering): (**a**) radar hood point cloud before coordinate alignment, (**b**) radar hood point cloud after initial alignment, (**c**) radar hood point cloud aligned through \(Z\)-axis correction, and (**d**) bottom-up view of the radar hood point cloud after alignment. Based on the value obtained in Equation (10), denoted as \(m\), if \(m\) is determined to be negative, it indicates that the \(Z\)-axis is reversed. In such cases, a \(Z\)-axis correction is required, as shown in Equation (11), resulting in the aligned point cloud set \(P^{\prime\prime}\). Figure 5b illustrates the initial alignment of the point cloud and the \(Z\)-axis direction correction, leading to the aligned point cloud depicted in Figure 5c. \[P^{\prime\prime}=P^{\prime}*R_{1} \tag{11}\]  
where \(R_{1}= \begin{bmatrix}1&0&0\\ 0&1&0\\ 0&0&-1\end{bmatrix} \).
## 2 Results
### Ray-Tracing Simulation and Time Mapping  
The program SIM-MX by [PERSON] [57] was used to simulate still diffraction images from a single PYP crystal (2 PHY.pdb) with experimentally determined parameters such as unit cell dimensions and mosaic spread [58]. The full list of input values for the program can be found in Table 1 and the definition of each is described in the original publication [57]. As stills are not implemented in SIM-MX, these were simulated by using a very small oscillation range (0.01\({}^{\circ}\)). One \"still\" image was generated for each of the 20 time bins by tracing 10,000 rays per frame: the simulation wavelength and its standard deviation were given by the mean IF and \(\mathrm{FWHM_{in}}\) of each bin. These stills were then summed to obtain a diffraction pattern for the polychromatic, chirped pulse. The reflections within this image can be tagged according to their energy, and thus according to their time of arrival at the crystal. In Figures 7 and 8, intensities on the diffraction pattern are color-coded based on the energy that stimulated them, which allows one to relate them to their time within the pulse. Diffraction images were first simulated using the 1.5% chirped central slice of the SwissFEL pulse. A second simulation was then run with a hypothetical 10% chirp to emphasize the achievable effect. The diffraction patterns show new reflections appearing during the duration of the pulse as well as low-resolution reflections that contain multiple energy contributions (Videos S1-S3). The observable intensity gradients are significantly enhanced by the higher chirp (Figure 8).  
Figure 5: Predicted integration of two reflections at different resolutions for 0.3\({}^{\circ}\) mosaicity between 55 and 57 fs after arrival of the pulse at the sample. The mean instantaneous bandwidth in this interval is 22 eV. The decrease in IF due to the linear chirp across the interval is also shown. **(a)** Partiality as a function of time (**b**) “Instantaneous intensity” as a function of time normalized to the partiality.  
Figure 6: Properties set by \(\mathrm{BW_{in}}\). **(a)** Instantaneous duration set by the mean \(\mathrm{FWHM_{in}}\) for each time bin. These intervals are all smaller than the 200-as set by the single SASE spike width. **(b)** Energy/time points expected per reflection at different times in the pulse and different resolutions (up to 5 Å). All bins at resolutions lower than 5 Å were assigned a value of 10 and are not shown in the plot.  
Figure 7: Chirped pulse diffraction. Simulated diffraction patterns for the central 20-fs of the SwissFEL pulse and for a hypothetical 10% chirp. Different energy contributions are colored based on their time of arrival for the smaller chirp. As a simulation for 10% chirp at SwissFEL is not available, the streaking effect is shown without specific time assignments. The simulated diffraction extends to a resolution of 1.5 Å.
# Antitumor Effects of _Cannabis sativa_ Bioactive Compounds on Colorectal Carcinogenesis
## 3 Bioactive Compounds in the Prevention and Treatment of Colorectal Cancer
### Effects of Cannabinoids in CRC-Associated Mechanisms
#### 3.1.4 Minor Cannabinoids  
Despite being less studied, minor cannabinoids such as CBDV, CBL, CBGV, CBCA, THCV, and CBGA revealed positive effects in the cell survival and proliferation of CRC cells. [PERSON] et al.'s research [65] indicated that the combinations of CBCA (14.5 or 29 uM), CBDV (23.5 or 47 uM), THCV (20 or 40 uM), and CBGA (25.6 or 51.2 uM) triggered a decrease in the cell viability of cells derived from human polyps. The combination of CBCA and CBDV also had synergistic effects, as well as the combination of THCV and CBGA. In HCT116 cells, data revealed cell viability proportions of 93, 87, and 51% with CBDV; 69, 44, and 45% with CBL; and 93, 75, and 71% for CBGV treatments at 2.5, 5, and 10 uM, suggesting that these cannabinoids exerted antiproliferative effects against CRC cells [50]. Despite the antiproliferative properties demonstrated for the cannabinoids CBCA, CBDV, THCV, and CBGA, the concentrations were relatively high compared to those of CBD, THC, and CBG, suggesting that their effects are not very strong. Moreover, studies have confirmed that these cannabinoids possess a variety of anti-inflammatory and anticancer properties [32; 33; 34], but again, their benefits on CRC were not significant, which emphasizes the importance of further researching and understanding the dose-related effects of minor bioactive compounds from _C. sativa_.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cannabinoids** & **Cell Line/Animal Model** & **Doses** & \begin{tabular}{c} **Cancer-Associated** \\ **Mechanisms** \\ \end{tabular} & \begin{tabular}{c} **Effects on CRC** \\ \end{tabular} & **Refs.** \\ \hline \multirow{6}{*}{HCT-116, SW480, and SW620} & \multirow{2}{*}{0-10 uM} & \
\begin{tabular}{c} Cell survival \\ Proliferation \\ Apoptosis \\ \end{tabular} &
\begin{tabular}{c} **Cell** viability, proliferation (2.5, 5, 10 uM), cyclin D1/D3, CDK2, \\ CDK4, CDK6, **\%** G1 phase arrest, p-elF2a, ATF3, ATF4, cleaved \\ caspase-3/7 and PARP \\ \end{tabular} & [50] \\ \cline{2-5}  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cannabinoids** & **Cell Line/Animal Model** & **Doses** & \begin{tabular}{c} **Cancer-Associated** \\ **Mechanisms** \\ \end{tabular} & \begin{tabular}{c} **Effects on CRC** \\ \end{tabular} & **Refs.** \\ \hline \multirow{8}{*}{THC} & \multirow{4}{*}{\begin{tabular}{c} Onaaliplatin-resistant DLD-1 \\ and col2050 \\ BALS/ c c c c c c c} (Cen: col2050) \\ \end{tabular} & \multirow{4}{*}{\begin{tabular}{c} Onaplicial-resistant DLD-1 \\ and col2050 \\ \end{tabular} } & \multirow{4}{*}{\
\begin{tabular}{c} Apoptosis \\ Oxidative stress \\ In vitro: 10 mg/kg \\ Mitochondrial \\ dysfunction \\ \end{tabular} } &
\begin{tabular}{c} In vitro: \& \# proliferation, AKT, TOR, AMPK, NOS3, NO, SOD; \\ \# cell death, LC3, p62, rapamycin, autophagic cells, ROS, \\ mitochondrial dysfunction \\ \end{tabular} & [55] \\
## 3 Results  
To our knowledge, this is the first widely available paper where four muscles, the UT, MT, PM, and PD, have been characterized using Myoton-3 during three postures NSP, RSP, and HSP. A few partially relevant outcomes are accessible in the literature for the considered muscles and postures, mainly HSP. In terms of basic features, the groups did not differ; however, those with HSP had a significantly greater HSA (Table 1). An independent sample \(t\)-test was performed to compare the effect of mechanical parameters on muscles in the NSP and RSP groups. The biomechanical characteristics of RSP are shown in Table 2 In terms of muscle tone, the UT, MT, and PM muscles are statistically significant (\(p<0.05\)). For the UT, these are the scores for the NSP (M = 18.6, SD = 4.7) and RSP (M = 14.8, SD = 1.8) groups, t (38) = 3.5, \(p=0.002\); these are the MT scores for the NSP (M = 13.9, SD = 2.3) and RSP (M = 18.2, SD = 6.0) groups, t (38) = \(-\)3.0, \(p=0.006\); these are the PM scores for the NSP (M = 20.1, SD = 4.0) and RSP (M = 12.4, SD = 3.1) groups, t (38) = 6.9, \(p\leq 0.001\). Conversely, PD is not statistically significant, \(p=0.06\). Therefore, the test revealed that the NSP and RSP groups have an effect on muscle tone in the UT, MT, and PM, while they exhibit no effect on the PD muscle. With respect to stiffness, the PM and PD muscles are statistically significant (\(p<0.05\)). The PM scores for the groups are: NSP (M = 235.9, SD = 11.7) and RSP (M = 201.1, SD = 45.8), t (38) = 3.2, \(p=0.003\). Similarly, the PD scores for the groups are: NSP (M = 309.9, SD = 70.7) and RSP (M = 348.1, SD = 68.7), t (38) = \(-\)1.7, \(p=0.046\). In contrast, UT and MT are not statistically significant, \(p=0.248\) and \(p=0.065\). Thus, the findings indicate that the NSP and RSP groups have an effect on PM and PD muscle stiffness, but have no effect on UT and MT muscle stiffness. In terms of elasticity, UT and MT are statistically significant (\(p<0.05\)). UT scores for the NSP group are (M = 1.2, SD = 0.3) and the RSP group are (M = 1.6, SD = 0.2), t (38) = 3.8, \(p=0.001\). Additionally, the MT scores for the NSP group are (M = 1.7, SD = 0.3) and RSP group are (M = 1.0, SD = 0.008), t (38) = \(-\)9.4, \(p\leq 0.001\). On the other hand, PM and PD are not statistically significant, \(p=0.143\) and \(p=0.302\). This shows a difference in elasticity between the NSP and RSP groups, which influences the UT and MT muscles, but not between the PM and PD muscles. The results of statistical analysis performed to compare the mechanical parameters in the NSP and HSP group are shown in Table 3 MT, PD, and PM muscles are statistically significant (\(p<0.05\)) in muscle tone. MT scores for the NSP group are (M = 13.9, SD = 2.3) and HSP group are (M = 11.2, SD = 3.1), t (38) = 3.0, \(p=0.004\), PM scores for the NSP group