esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Cooperative Spectrum Sensing Based on Deep Multi-User Reinforcement Learning
## 5 Conclusions  
This paper studies the multichannel access problem by proposing deep multi-user reinforcement learning. To reduce the conflict of user access channels and the high complexity caused by large state space, we consider a cooperative strategy and propose a dynamic multi-channel cooperative sensing algorithm based on DDQN. The proposed algorithm can achieve better reward performance with faster convergence speed than other algorithms based on Q-learning and DQN, especially in large networks. In our algorithm, we train DDQN for all users individually, and each user judges whether there is a conflict through the ACK signal. Comparing with other DSA methods, the proposed DMRL consider multi-user access and a cooperative DSA network under the presence of spectrum sensing errors. We conducted simulation experiments using publicly available real communication data sets. The simulation results verify the superior performance of the proposed algorithm, which can promote the development of the CR technology to achieve more efficient utilization of spectra. Our algorithm has a lower conflict rate and gets higher rewards in the multi-user case.  
Conceptualization, S.L. and J.H.; methodology, S.L., J.W. and J.H.; software, J.W.; validation, J.W. and J.H.; writing--original draft preparation, J.H.; supervision, S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 61703328), the China Postdoctoral Science Foundation funded project (No. 2018M631165), Shaanxi Province Postdoctoral Science Foundation (No. 2018 BSHYDZZ23) and the Fundamental Research Funds for the Central Universities (No. XJ2018254).  
Not applicable.  
Data Availability Statement: Data is not publicly available.  
The authors declare no conflict of interest.
# Modeling and Compensation of Motion Errors for 6-DOF Robotic Manipulators
## 3 Trajectory Compensation Based on Stiffness Modeling
### Error Compensation Model for a 6-DOF Manipulator
#### 3.3.3 Motion Error Compensation by Correcting Joint Angle  
According to the stiffness formula derived previously, when the stiffness and the generalized force \(F\) at the end are known, the end error \(\Delta\delta\) can be calculated:  
\[\Delta\delta=\Delta(\delta_{l}+\delta_{\theta})=\frac{F}{C}+\Delta\delta_{l} \tag{30}\]  
\[\Delta\delta=J(q)K_{\theta}^{-1}J^{T}(q)F+\Delta\delta_{l} \tag{31}\]  
When there is no external load on the robot, the joint angle is regarded as the theoretical joint angle \(\theta_{i}\), and its end position is the theoretical position \(s_{i}\). When the robot is under an external load, its joint angle and end position will deviate due to its stiffness, the joint angle is the actual value \(\theta_{r}\), and the end position is the actual position \(s_{r}\). The deformation \(\Delta s\) at the end of the robot subjected to external load causes the error of the end position. In this paper, the error is compensated by correcting the joint angle \(\theta_{c}\) by \(\Delta\theta_{c}\).  
\[\Delta\theta_{c}=J^{-1}(q)\Delta s \tag{32}\]
# A Precise Modeling Method for PMSM Servo System of Spacecraft
## 3 Nonlinear Precise Modeling
### Friction Characteristics Modeling
In this case, the modified LuGre friction model is consistent with the conventional model.
## 2 Study Area and Data
### Hj-1A/B Overview
#### 2.2.1 Sensors  
The payload of both HJ-1A and 1B includes two identical wide-coverage multi-spectral CCD cameras. The two CCD cameras were placed symmetrically to each other and the satellite nadir point. The placement enables the two CCD cameras to equally divide the Field Of View (FOV) and observe the earth side by side. The swath width of a single HJ-1A/B camera is 360 km, while the combination  
Figure 1: Location of the study area. The satellite image on the left is the subset of HJ1B CCD2 image acquired on 16 August 2010 shown with true color compositing (bands 3, 2, 1 in red, green and blue).  
of two cameras obtains a swath width of 700 km. The spatial resolution of the CCD image (referred to as HJ-1A/B images in the following) is 30 m, with three visible bands (430-520 nm, 520-600 nm, 630-690 nm) and one near-infrared band (760-900 nm) [27]. The wide coverage, fine spatial resolution and high observation frequency of HJ-1A/B images enable them to capture the rapid changes of the land surface. Table 1 briefly outlines a comparison between HJ-1A/B and Landsat TM/ETM. For details about the imaging mechanism of HJ-1A/B, see [26].  
Figure 2 showed the relative Spectral Response Functions (SRF) for the equivalent reflective bands of ETM+, four HJ CCD cameras and MODIS. The central wavelength and band width between HJ CCD and Landsat ETM+ are very close. However, the SRF of MODIS is markedly narrower than that of the corresponding ETM+ and HJ CCD bands.
## 4 Discussion
### Burr Length (Down Milling)  
Table 8 provides the ANOVA results for burr length in the context of micro-milling. The foremost contributor to the variability in burr length was identified as the feed rate (F \(\upmu\)/tooth), contributing significantly with a substantial 77.70%. This was substantiated by a high F-value of 46.43 and an exceptionally low \(p\)-value of 0.000, underscoring its statistical significance. This emphasizes the crucial role of the feed rate in determining the length of burrs during micro-milling. Additionally, cutting speed (Vc m/min) was found to be a significant factor, contributing 7.77%, with an F-value of 4.64 and a \(p\)-value of 0.035. This suggests that variations in cutting speed have a discernible impact on burr length. In contrast, the axial depth of cut (ap \(\upmu\)m) was deemed non-significant, contributing 5.33% and with a \(p\)-value of 0.081. The error term accounted for 9.20% of the variability.  
Figure 9 illustrates the main effects plot for burr length. It is evident that burr length was inversely related to feed rate; as feed rate increased, burr length decreased. This phenomenon is explained by the dominance of the ploughing mechanism at lower feed rates, causing plastic deformation and resulting in larger burr size. Conversely, at higher feed rates, the shearing mechanism predominated, leading to smaller burr lengths due to burr chipping. It is also evident from Figure 8 that burr length decreased with increasing cutting speed. Higher cutting speeds generally resulted in reduced material deformation. As the cutting tool moved through the material at a faster rate, there was less time for the material to deform plastically. This reduction in plastic deformation contributed to a decrease in the formation of burrs. Moreover, higher cutting speeds promoted more efficient chip formation, which in turn optimized the burr length. The increased speed allowed the tool to cut through the material with a cleaner shearing action, resulting in well-defined chips instead of material being pushed or dragged, which is associated with burr formation. The standard deviation for burr length is displayed in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & **Contribution** & **F-Value** & **\(p\)-Value** & **Significance** \\ \hline F (\(\upmu\)m/tooth) & 77.70\% & 46.43 & 0.000 & Significant \\ Vc (m/min) & 7.77\% & 4.64 & 0.035 & Significant \\ ap (\(\upmu\)m) & 5.33\% & 3.18 & 0.081 & Non-significant \\ Error & 9.20\% & & & \\ \hline Total & 100.00\% & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: ANOVA table for burr length.  
Figure 9: Main effects plot for burr length.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 5 Experiments and Results
### Noise Impact Assessment  
Figure 17 presents identical processing under realistic noise conditions, where additive noise is introduced at the VCO stage to simulate practical implementation challenges. The comparison reveals observable degradation in signal quality throughout the processing chain, with noise propagation affecting both the PPM generation accuracy and demodulation fidelity. Despite the presence of noise, the fundamental PPM structure remains recognizable, indicating inherent robustness of the position-modulation approach.
## 4 Experiment Setup and Performance Analysis
### Experiment Results and Analysis in the Park Scenarios  
To analyze the performance of the macrodiversity reception scheme comprehensively, experiments were carried out in the researched park. The two local convergence nodes (LCN1 and LCN2) of the sink node were 30 m apart and placed at an open location in the park. Each local convergence node was equiped with four antennas to form a cluster. The height of antenna was about 1.5 m, and any two antennas were more than one meter apart. Experiments were divided into two parts in the park scenarios. The first experiment analysed performance of macrodiversity reception scheme using different number of antennas and the second experiments compared the performance of diversity scheme using different antenna placement strategies in the park scenarios. We put the sensor node aside by the road for testing during the first measurement. The location of the sensor node was selected 800 m from the sink node and a test location was added every additional 100 m. In the second experiment, we selected four locations in the researched park separately. At each location, we tested the transmission performance of diversity scheme using different antenna placement methods. The specific implementation steps included three parts. First, the two local convergence nodes were about 30 m apart and we validated the performance of macrodiversity reception scheme with two clusters using four antennas in the sink node. Second, the overall eight antennas in the sink node were placed at LCN1 to form a cluster using eight antennas, and the performance of single cluster reception scheme in LCN1 was analyzed. Finally, the overall eight antennas in the sink node were placed at LCN2 to test the performance of the single cluster reception scheme in LCN2. A total of 3000 frames were measured at each location during measurements. The locations of sensor node and local convergence node in the two experiments are shown in Figure 7. The scene of the sink node and the sensor node are shown in Figure 8.  
Figure 6: Local convergence module and convergence center.  
Figure 7: Locations of sensor node and local convergence node in the park scenarios.
# Numerical Study of Mode Conversion between Fundamental Lamb and Rayleigh Waves
## 3 Results
### Lamb Wave to Rayleigh Wave Conversion  
#### 3.1.1 Incident \(A_{0}\) Mode  
A numerical visualization of the nodal displacement vectors for the wave propagation for an incident \(A_{0}\) mode is shown in Figure 4a. The \(A_{0}\) mode excited in the thin plate propagates and interacts with the discontinuity \(D_{1}\), where part of the energy is reflected as \(A_{0r}\) and \(S_{0r}\) and part of it is transmitted into the quarter-space, where it mode converts into the Rayleigh mode \(A_{0}R\). When the \(A_{0}\) mode interacts with \(D_{1}\), it also gives rise to circular P and SV wave fronts radiating through the quarter-space, as shown in Figure 4a. Furthermore, the SV wave front amplitude is at a maximum in a \"shear window\", and there is a phase reversal at the center of the window. Such a radiation pattern has been shown previously for a vertical line load acting on top of a half-space [22]. The power coefficients were calculated using Equation (3) and plotted in Figure 4b. The results show that \(>\)50% of the incident \(A_{0}\) energy is scattered, but 34% is converted into the R mode. The reflected energies in the plate are Lamb modes, \(A_{0R}\) and \(S_{0R}\), the transmitted Rayleigh mode in the half-space is \(A_{0}R\) and the scattered modes are \(P_{s}\). Additionally, another R wave can be seen propagating along the vertical edge of the quarter space. By definition of the scattering power coefficient, this additional wave will be included in the power coefficient analysis.  
Figure 3: (**a**) The input pulse used for excitation, (**b**) example A-Scan showing the \(A_{0}\) and reflected disturbance in the time domain.
## 4 Experimental Evaluation
### Ablation Experiments  
#### 4.4.1 Experimental Setup  
To verify the effectiveness of the data enhancement method proposed in this study, we carried out ablation experiments by inputting different combinations of Shui manuscript datasets into Faster R-CNN. All the datasets were divided into training set and test set in a ratio of 7:3, and a small number of samples in the training set were selected to form the validation set. Table 6 shows the dataset information for each experiment.  
The initial learning rate was set to 0.001, the training iterations were 60,000, and the learning rate decayed to 0.0001 after 10,000 iterations. The momentum was set to 0.9. The batch size of RPN was set to 512.
# Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
## 3 Related Work
### Comments  
Analyzing existing references in the literature on the key device positioning problem helps to identify the different strategies evaluated by different authors in their research, the difficulties encountered, and the successes achieved with the experiments. Each study has its original purpose of proposing a method or approach that best helps solve the positioning problem in different contexts. They may contain particularities specific to the study's region or the network technology used when the survey was developed.  
The consulted references include recent works and older research chosen by their relevance in the area. From them, it is possible to extract insights capable of helping in developing new works, shortening distances, and, in a way, contributing to the creation of new research that will be part of the so-called state of the art. Concepts such as candidate positions, clustering and network partitioning approaches, techniques for finding the shortest path in graphs, or the use of a grid to minimize the number of candidate positions to be evaluated were identified in the consulted references and helped in the building of strategies for the development of our research.  
Among the positioning strategies, heuristic approaches are evidenced in several studies. It is motivated by the complexity of the scenario involved (either by the number of devices involved or by the high set of constraints associated with the problem) and by the recognized fact that such strategies, if well implemented, ensure a final solution capable of meeting all established requirements.  
Based on this, among the points that we defined as strategic to be explored by our research, we included: (a) The experimentation of large-scale scenarios because we identified many studies using reduced experimentation datasets; (b) A detailed assessment of the topographical profile of the terrain for the calculation of losses, as we identify the existence of works that either consider only the distance between elements, or path loss models that use a general classification for the terrain under analysis; (c) Prioritization of the use of pole positions that contain DAs installed, with the objective of stimulating the positioning of key devices of the AMI network (in this case, routers and gateways) at points that will be mandatorily served by communication elements of the Backhaul network (in this case, positions that should provide for the installation of routers of the backhaul network) and, therefore, minimize the number of devices to be installed, since both automation equipment (DAs) and key devices of the AMI network can share the same equipment.  
Finally, proposing a heuristic strategy for positioning aims to generate a method that is easy to understand, capable of meeting the requirements of the problem, and obtaining a solution with received power quality within established limits. Therefore, heuristics are proper even for large-scale problems representing real smart grid scenarios.
## 6 Discussion
### Statistical Tests
\begin{table}
\begin{tabular}{c c c} \hline \hline Target attribute S: & & \\ \hline Used attributes for \(\mathrm{X_{A}}\) & Used attributes for \(\mathrm{X_{B}}\) & \(p\)-value \\ \hline SHMU & SHMU + Day of Year & 0.000238 \\ SHMU & SHMU + Satellites & 0.000179 \\ SHMU + Day of Year & SHMU + Day of Year + Satellites & 0.000617 \\ SHMU + Satellites & SHMU + Day of Year + Satellites & 0.000698 \\ \hline Target Attribute Sr: & & \\ \hline Used attributes for \(\mathrm{X_{A}}\) & Used attributes for \(\mathrm{X_{B}}\) & \(p\)-value \\ \hline SHMU & SHMU + Day of Year & 0.000297 \\ SHMU & SHMU + Satellites & 0.000119 \\ SHMU + Day of Year & SHMU + Day of Year + Satellites & 0.000917 \\ SHMU + Satellites & SHMU + Day of Year + Satellites & 0.002740 \\ \hline Target Attribute g02: & & \\ \hline Used attributes for \(\mathrm{X_{A}}\) & Used attributes for \(\mathrm{X_{B}}\) & \(p\)-value \\ \hline SHMU & SHMU + Day of Year & 0.000249 \\ SHMU & SHMU + Satellites & 0.000314 \\ SHMU + Day of Year & SHMU + Day of Year + Satellites & 0.012501 \\ SHMU + Satellites & SHMU + Day of Year + Satellites & 0.006714 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of statistical permutation tests, which show that additional satellite-derived attributes and the measurement date have ensured a significant increase in the accuracy of trained regression models. attributes (marked in the table as Satellites) on the other. In the case of target variable S, the measurement date provides a lower increase in model accuracy than the satellite attributes. This follows from the fact that the \(p\)-value for the first permutation test in Table 5 (SHMU versus SHMU + Day of Year) is higher than the one for the second permutation test (SHMU versus SHMU + Satellites): the lower \(p\)-value means it is less likely that the two compared models have identical means. Somewhat surprisingly, target variable g02 behaves differently: here, the measurement date provides a more significant increase in accuracy (evidenced by the lower corresponding \(p\)-value) than the group of satellite attributes. This means that although the target variables jointly capture various aspects of local environmental pollution, each of them has its own individual behavior and dynamics, significantly different from the other two. These differences can also be gauged from the differing accuracies of the trained models for S, Sr, and g02, listed in Tables 2-4 above.
# Curcumin Innovative Delivery Forms: Paving the Yellow Brick Road' of Antitumoral Phytotherapy
## 2 Biological Actions of Curcumin
### Molecular Targets of Curcumin
#### 2.2.1 Curcumin Modulates the Activity of Transcription Factors
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Family** & **Molecular Target** & **Effect** & **Ref** \\ \hline \multirow{8}{*}{Transcription factors} & NF-kB & \(\downarrow\) & [78; 79; 80; 88; 95] \\  & Nrf2 & \(\uparrow\) & [96] \\  & AP-1 & \(\downarrow\) & [83; 84; 85; 86; 87; 88] \\  & STAT-3 & \(\downarrow\) & [97; 98] \\  & STAT-5 & \(\downarrow\) & [99; 100] \\  & \(\beta\)-catenin & \(\downarrow\) & [101; 102] \\  & EGR-1 & \(\downarrow\) & [103; 104] \\  & HIF-1 & \(\downarrow\) & [105] \\  & Notch-1 & \(\downarrow\) & [106] \\ \hline \multirow{8}{*}{Growth factors} & EGF & \(\downarrow\) & [107] \\  & FGF & \(\downarrow\) & [108] \\  & PDGF & \(\downarrow\) & [109] \\  & TGF-\(\beta\) & \(\downarrow\) & [110; 111; 112; 113; 114] \\  & VEGF & \(\downarrow\) & [115; 116; 117] \\ \hline \multirow{8}{*}{Cytokines, pro-inflammatory} & TNF-\(\alpha\) & \(\downarrow\) & [95; 118; 119; 120] \\  & IL-1 & \(\downarrow\) & [121] \\  & IL-2 & \(\downarrow\) & [122] \\  & IL-5 & \(\downarrow\) & [123] \\  & IL-6 & \(\downarrow\) & [118] \\  & IL-8 & \(\downarrow\) & [121] \\  & IL-12 & \(\downarrow\) & [124] \\  & IL-18 & \(\downarrow\) & [125] \\ \hline \multirow{8}{*}{Enzymes} & COX-2 & \(\downarrow\) & [72; 80; 126; 127] \\  & iNOS & \(\downarrow\) & [127] \\  & Lipoxygenase & \(\downarrow\) & [128] \\  & MMP-9 & \(\downarrow\) & [78; 129; 130; 131] \\ \hline \multirow{8}{*}{Kinases} & JNK & \(\uparrow\) & [132] \\  & MAPK & \(\downarrow\) & [133] \\  & PKC & \(\downarrow\) & [131] \\  & Akt & \(\downarrow\) & [134] \\  & CDKs & \(\downarrow\) & [135] \\ \hline \multirow{2}{*}{Receptors} & AR & \(\downarrow\) & [86] \\  & EGFR & \(\downarrow\) & [79; 119] \\ \hline \multirow{3}{*}{Adhesion molecules} & ICAM-1 & \(\downarrow\) & [95] \\  & VCAM-1 & \(\downarrow\) & [95] \\  & ELAM-1 & \(\downarrow\) & [95] \\ \hline \multirow{2}{*}{Antiapoptotic proteins} & Bcl-2 & \(\downarrow\) & [136; 137; 138; 139; 140] \\  & Bcl-xL & \(\downarrow\) & [136; 138; 141] \\ \hline \multirow{2}{*}{Proapoptotic proteins} & Bax & \(\uparrow\) & [136; 137; 138; 140] \\  & Bak & \(\uparrow\) & [140] \\ \hline \multirow{2}{*}{Others} & Cyclin D1 & \(\downarrow\) & [142; 143] \\  & p53 & \(\uparrow\) & [144; 145] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Molecular targets and cell processes modulated by curcumin.
# Business Intelligence through Machine Learning from Satellite Remote Sensing Data
## 3 State of the Art and Related Products
### State of the Art: Key Takeaways  
Taking all of the above into consideration, we realize the importance of satellite remote sensing and machine learning. We presented various successful applications from the agriculture, tourism, energy production, and urban studies domains that can greatly benefit many countries. These studies offer insights into the efficient combination of data science and satellite imagery by providing information about the many satellite data products and their benefits and limitations. Various spectral indices have been evaluated depending on the case study, including vegetation, water, and imperviousness, with NDVI being the most commonly used across all sectors. This is probably due to its conceptual simplicity and 'tangibility'. Moreover, nighttime light imagery, even with its reduced spatial resolution, can be a good estimator of economic growth and human activity in the majority of sectors.  
These various studies also provide methodologies for satellite data acquisition and prepossessing techniques and machine learning model evaluation metrics. Through this, albeit not extensive, literature review, we can derive the growing interest in the use of Landsat and Sentinel products. These images are of high to medium spatial resolution and, compared with private endeavors, can be limiting in cases where extremely high precision is required (e.g., military). On the other hand, they are free even for commercial usage and hence easily accessible by most businesses and organizations. Their accessibility and interoperability, which were showcased via their combination in several research works, coupled with their proven ability to estimate economic trends and detect and forecast changes in Earth's landscape, as well as human activity, make them ideal for our software.  
Additionally, the increasing use of Deep Learning methods, as well as the rethinking of older machine learning methods (e.g., Random Forests) in the remote sensing sphere, is another main point in the existing literature. Both approaches have their advantages and disadvantages (e.g., computational constraints, simplicity, robustness, scalability, etc.), but both can automate several procedures while offering knowledge for decision making in the public and private sectors. In the case of our approach, taking these characteristics into account, we chose to implement shallow machine learning methods that do not require huge computational capabilities. This is due to the fact that our proposed software needs to be able to execute in completely free cloud platforms that often have restrictions.  
Finally, as far as Greece is concerned, the solutions provided by 'local' companies are, to the best of our knowledge, narrowly limited to the agricultural and weather forecasting sectors. Furthermore, and this is prevalent in all the aforementioned commercial products, they are 'locked' behind paywalls, making them prohibitive for the majority of SMEs.