esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Hardware Composition of Performance Testing System for Space Harmonic Reducer
### Hardware Principle of the System  
The overall hardware schematic of the space harmonic reducer performance test system is shown in Figure 3.  
The test cabinet of performance test device for space harmonic reducer is made up of 6 servo motors and drivers. The 6 drivers are connected by the CANOpen bus. Based on software, the host computer sends operation instructions to any main driver via CAN bus, so that the main driver can specify any motor action. The angle sensor can input the signal through the PCI card connected to the PCI slot of the host computer, and a PCI card has two channels, which can connect two angle sensors and provide the underlying driver and API function interface. Torque sensor can be connected to the computer host by CAN port. Two torque sensors share one CAN bus to connect to the computer.  
In the over current/voltage protector of the magnetic powder brake, direct current value can be detected by current sensor based on RS485 bus (the maximum current of the magnetic powder brake is about 1A), and the control signal can be output by comparing the acquisition value with the set value to protect the magnetic powder brake and motor from  
Figure 2: The principle block diagram of space harmonic drive gearing test system.  
over-current. The whole test device is composed of sensors, motor drivers, a dual-port CAN card, and a control computer, which completes the automatic control function.
# An Efficient and Secure Fog Based Routing Mechanism in IoT Network
## 2 Literature Review
The paper introduces an Efficient and Secure Fog-Based Routing Mechanism (ES-FRM) to tackle the vulnerability of Internet of Things (IoT) networks to both internal and external attacks, as well as energy consumption challenges. Each IoT node calculates a comprehensive trust value for the next intermediate node, taking into account direct trust, indirect trust, and energy trust values before forwarding data. By comparing this trust value with a predefined threshold, the system identifies rogue nodes and ensures network protection.
## 5 Simulation Analysis
### Simulation Results of S11 Reflection Coefficient With and Without  
The findings of the simulated study of the S11 reflection coefficient demonstrate a considerable technical difference in the performance of the antenna design with and without the block (Figure 15: (a) with the block and (b) without the block). The S11 parameter, measured in decibels (dB), measures the reflection of the signal input towards the source, with lower values indicating enhanced impedance matching and less signal reflection. In the scenario including the block, the antenna attains a reflection coefficient of \(-40\) dB at 4 GHz, signifying little signal reflection. This indicates the antenna is almost optimally matched at this frequency, enabling efficient energy transmission with little loss. Additionally, the performance is enhanced further at an elevated frequency of 13.7 GHz, achieving a reflection coefficient of \(-50\) dB. This deficient number signifies outstanding impedance matching, facilitating the transmission of almost all input power without reflection and enhancing the antenna's performance at elevated frequencies.  
In contrast, the antenna design lacking the block exhibits somewhat elevated reflection coefficients. At 4 GHz, the reflection coefficient measures \(-28\) dB, indicating a significant increase in signal reflection relative to the design using the block. This suggests that impedance matching is less efficient in the absence of the block, resulting in increased signal reflection. At 13.7 GHz, the reflection coefficient enhances to \(-43\) dB, surpassing the performance at 4 GHz, but it remains inferior to the comparable value with the block. This indicates that while the antenna devoid of the block has adequate performance, its efficiency is inferior to that of the block-integrated design, especially at elevated frequencies. Incorporating the block into the antenna design markedly improves impedance-matching  
Figure 14: S21 transmission coefficient with block.  
Figure 15: S11 Reflection coefficient (**a**) with a block; (**b**) without block.  
properties, particularly at pivotal frequencies like 4 GHz and 13.7 GHz. The block's presence reduces signal reflection, enhancing performance, especially at elevated frequencies where the reflection coefficient is lowered to levels as low as \(-50\) dB. This modification emphasizes the block's function for improving the antenna's overall efficiency, making it an essential component in applications requiring high-frequency performance and low energy loss.
## 2 Material and Methods
### Biodegradability Analysis  
The foam biodegradability was determined by measuring the mass loss of material exposed to the soil, following the ASTM 6400-99 Standard (Standard Specification for Compostable Plastics) with some modifications. Seven samples of each foam composition were buried 3 cm from the surface in a container containing commercial black soil composed of black soil, fertilizer, organic vegetable compost, and limestone. The containers were kept at room temperature under aerobic conditions and water (100 mL) was sprayed on the samples once a day to ensure the system's humidity throughout the experiment. The test lasted 21 days and the samples were recovered, cleaned, and weighed every seven days. This loss of mass resulted from the actions of microorganisms and chemical reactions. After each weighing, the samples were reintroduced to the soil for further testing.
# Application of Metal Magnetic Memory Testing Technology to the Detection of Stress Corrosion Defect
## 2 Experimental Section
### Specimen Preparation  
The specimens were made by 0.45% C steel in this study, which is medium-carbon structural steel and widely used in the structural engineering field. Composition of 0.45% C steel, % by weight is C (0.42-0.50%), Si (0.17-0.37%), Mn (0.50-0.80%), P (\(\leq\)0.035%), S (\(\leq\)0.035%). Its mechanical properties are listed in Table 1. Flat dog-bone specimens designed based on GB/T228-2002 were prepared without (used in corrosion experiments) and with a groove (used in the static tensile experiment). The rectangular groove (6.8 mm width and 2 mm depth) was carefully made by a linear cutting machine in the middle of the specimens. The specimen was designed to be a special shape (dog bone) to obtain a uniform distribution of stress in the corrosion area.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Material** & **Yield Strength \(\sigma_{s}\) (MPa)** & **Ultimate Strength \(\sigma_{b}\) (MPa)** & **Elongation Rate \(\delta_{5}\)(\%)** \\ \hline
0.45\% C steel & \(\geq\)355 & \(\geq\)600 & 16 \\ \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of 0.45% C steel.  
The shape and size of the 6-mm-thick specimens used in the experiments were drawn in Figure 1. Five scanning lines with a length of 100 mm, denoted as Line 1, Line 2, Line 3, Line 4, and Line 5, were parallel to each other at intervals of 4.5 mm. Line 3 is the centerline of the specimen.  
Before experiments, all of the specimens were polished to grade P1000 grit, followed by rinsing with isopropyl alcohol, drying in warm air, and weighing with an electronic balance. The specimen was polished only to remove the corrosion on the specimen surface and measure the mass loss of the specimen more accurately. In addition, every specimen was demagnetized before it was loaded or corroded to eliminate the effect of mechanical processing on the magnetic field.
# Color Structured Light Stripe Edge Detection Method Based on Generative Adversarial Networks
## 5 Conclusions  
In this paper, a color structure light stripe edge detection method based on generative adversarial networks, which is named horizontal elastomeric attention residual Unet-based GAN (HEAR-GAN), is proposed. Additionally, a specific dataset was designed for training and testing the model. Firstly, a De Bruijn sequence-based color stripe pattern and a multi-slit binary pattern were designed. After that, the images of objects with these patterns were captured under room lighting conditions. Among them, the images with a multi-slit pattern after being preprocessed were selected as the ground-truth images. The images with a color stripe pattern and their respective ground-truth images were cut into patches. Finally, some data augmentation techniques were applied to these patches, and then they were divided into a training set and a validation set. Using the images of objects with a multi-slit pattern as ground-truth images reduced not only the labor of manual annotation but also enhanced the quality of the training set. Patches of the test images, after being predicted with the trained model, were merged together into large images. The centerlines in these resulting images were detected as the edges in the color structured light stripe images. The experimental results prove that the proposed method can overcome the challenges from ambient light noise and the effect of the complex characteristics of the object surface so that most of the edges in the images of a color stripe pattern are detected. Additionally, the comparison with other GANs-based networks shows that our network had a higher color stripe segmentation performance than its counterparts. Moreover, the results of a statistical significance test with the MSE of different methods demonstrate that the proposed method achieved higher pixel local accuracy than the manual-based edge detection methods, i.e., the modified Canny and modified Sobel edge detectors.  
Methodology, C.X.; Validation, M.H.; Investigation, D.P.; Resources, D.P.; Data curation, D.P. and M.H.; Writing--original draft preparation, D.P. and M.H.; Writing--review and editing, C.X.; Supervision, C.X. 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, grant number 62073128.  
Not applicable.  
The participants of this study did not give written consent for their data to be shared publicly, so supporting data is not available.  
The authors declare no conflict of interest.
# Estimating Global Gross Primary Production Using an Improved MODIS Leaf Area Index Dataset
## 2 Methods and Data  
### Model Description  
The direct and diffuse components of the total incident radiation were partitioned, with this partitioning process being determined by the proportion of cloud cover [44; 45; 46]. We applied two-stream radiative transfer approximation, which accounted for multiple scatterings within a finite canopy [44; 46]. We implemented different scattering coefficients for direct and diffuse radiation, ensuring that the unique characteristics of the two radiation types were accurately represented [46]. The methods mentioned above allowed us to quantify the transmission and reflection factors for both direct and diffuse radiation, providing a detailed understanding of the interactions between canopy and radiation [46; 47]. We integrated the two-leaf model with canopy radiation transfer to quantify the radiation absorption for both sunlit and shaded leaves [48; 49]. The Ball-Berry-Leuning (BBL) stomatal conductance model was integrated into the photosynthesis process, optimizing the balance between CO\({}_{2}\) uptake and water conservation [50]. Moreover, the calculation of the exact duration of global photosynthesis through sunrise, sunset, and local time computations facilitated a more accurate estimation of GPP. Finally, given the hourly time resolution of the data, accurately calculating the specific times for photosynthesis during sunrise and sunset in the respective time zone required precise determination of the daylight proportion in that zone (Appendix A).  
We improved the corresponding variables of the Farquhar model for C3 plants [51] and the Collatz model for C4 [52] by integrating V\({}_{\text{max}}\), J\({}_{\text{max}}\), and Rd from the CLM model in the photosynthesis module [24; 53]. The parameters of V\({}_{\text{cmax25}}\), J\({}_{\text{max25}}\), and Rd\({}_{\text{25}}\) were scaled over the canopy for sunlit and shaded leaves and were adjusted for leaf temperature. The maximum rate of carboxylation at 25 \({}^{\circ}\)C (V\({}_{\text{cmax25}}\)) varied with foliage nitrogen concentration, the fraction of leaf nitrogen in Rubisco, and the weight proportion of Rubisco relative to its nitrogen content [23]. A flow chart of the implementation of the proposed model by parallel computation with GPU is presented in Figure 1.  
The proposed model incorporates seven input variables, but the forcing variables had to be resampled to align with the spatio-temporal resolution of the climate variables, as the spatio-temporal resolution for their metadata did not match (Table 2).  
Figure 1: Flow chart of the proposed model.
# Autonomous Target Tracking of UAV Using High-Speed Visual Feedback
## 2 Related Works  
A number of studies on controlling UAVs using visual sensors have been conducted in recent years [14]. In particular, studies on visual servoing for UAVs are reported in [15]. [PERSON] et al., proposed a multi-camera visual servo system for controlling unmanned micro helicopters by using visual information [16]. [PERSON] et al., presented a visual control method for a small UAV, in which binocular stereo vision and an ultrasonic system are fused to implement an obstacle avoidance strategy [17]. [PERSON] et al., proposed an eye-in-hand visual servo control system for stationary flight of a UAV [18]. [PERSON] et al., utilized a color-based object recognition method to chase a moving target from a flying UAV [19]. [PERSON] et al., proposed a pose estimation algorithm using one single circular landmark for controlling a quadrotor [20]. [PERSON] et al., presented a visual navigation strategy that enables a UAV  
Figure 1: Concepts of conventional approach (**a**) and relative-position-based visual servoing (**b**).  
to fly through various obstacles in complex environments [21]. [PERSON] et al., proposed a method of autonomous landing of a VTOL UAV on a moving platform using image-based visual servoing (IBVS) instead of calculating the position of the target [22]. [PERSON] et al., proposed an adaptive dynamic IBVS scheme for underactuated UAV control [23]. [PERSON] et al., proposed and compared two visual servoing schemes based on Hough parameters to track linear structured infrastructures [24]. [PERSON] presented a quadrotor system capable of autonomously landing on a moving platform using an onboard vision sensor for localization and motion estimation of the moving platform [25]. [PERSON] et al., proposed vision-based localization and servoing for a quadrotor to realize autonomous dynamic grasping and perching [26; 27]. In addition to these studies, for improving the flight performance of UAVs, a number of robust visual servo controller designs have also been presented [28; 29; 30; 31; 32].  
In previous studies, the highest reported onboard processing rate for the controller strategy was 75 Hz, which is not high enough to realize high-speed autonomous flight of a UAV. To achieve this goal, it is necessary to integrate a high-speed vision system into the UAV. A high-speed vision system is effective for controlling a robot at high speed, and various applications of high-speed vision have been developed [33; 34; 35; 36]. Moreover, a high-speed tracking system [37] and a high-speed velocity estimator for UAVs [38] have also been proposed.  
Considering the merit of the high noise tolerance of high-speed vision systems and the possibility for high-speed control, in this paper we propose a relative-position-based visual servoing method using an onboard high-speed camera to realize an autonomous target tracking task.
# You Only Attack Once: Single-Step DeepFool Algorithm
## 4 Experiments
### Experimental Results
#### 4.4.1 Experimental Comparison with Classical Algorithms
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithms** & **Datasets** & **Success Rate** & **L2 Norm** & **Steps** & **Time** \\ \hline PGD [26] & CIFAR-10 & 72.5\% & 41.25 & 10 & 0.005 s \\ FGSM [7] & CIFAR-10 & 63.5\% & 41.26 & 1 & 0.003 s \\ BIM [27] & CIFAR-10 & 55.2\% & 17.49 & 10 & 0.004 s \\ FFGSM [28] & CIFAR-10 & 72.1\% & 41.25 & 1 & 0.003 s \\ APGD [29] & CIFAR-10 & 58.19\% & 10.42 & 10 & 0.004 s \\ SparseFool [30] & CIFAR-10 & 91.0\% & 54.18 & 10 & 1.962 s \\ OnePixel [31] & CIFAR-10 & 47.9\% & 2.58 & 10 & 0.076 s \\ Pixel [32] & CIFAR-10 & 52.9\% & 12.66 & 10 & 0.129 s \\ PIFGSMPP [33] & CIFAR-10 & 82.3\% & 0.05 & 10 & 0.003 s \\ DeepFool [5] & CIFAR-10 & 23.15\% & 0.05 & 1 & 0.056 s \\ YOAO (Ours) & CIFAR-10 & 93.81\% & 3.83 & 1 & 0.062 s \\ \hline PGD & MNIST & 3.5\% & 0.61 & 10 & 0.003 s \\ FGSM & MNIST & 3.5\% & 0.65 & 1 & 0.003 s \\ BIM & MNIST & 4.1\% & 0.63 & 10 & 0.003 s \\ FFGSM & MNIST & 3.4\% & 0.64 & 1 & 0.003 s \\ APGD & MNIST & 4.1\% & 0.02 & 10 & 0.003 s \\ OnePixel & MNIST & 1.7\% & 0.16 & 10 & 0.616 s \\ Pixel & MNIST & 89.9\% & 6.04 & 10 & 0.723 s \\ DeepFool & MNIST & 30.2\% & 1.53 & 1 & 0.040 s \\ YOAO (Ours) & MNIST & 66.5\% & 9.01 & 1 & 0.091 s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of adversarial attack algorithms on classifiers.
# An Assessment of Anthropogenic CO\({}_{2}\) Emissions by Satellite-Based Observations in China
## 2 Data and Methodology
### Methodology
#### 2.3.1 Variable of XCO\({}_{2}\) Used for Estimation of Anthropogenic Emission  
The magnitude of XCO\({}_{2}\) include CO\({}_{2}\) emitted by anthropogenic activities, the fluxes of terrestrial biosphere, fluxes transported by atmospheric wind fields [27; 28] and CO\({}_{2}\) of regional background. We introduce therefrom an interannual variability by removing the regional background signal and calculating their annual mean to enhance the signals of CO\({}_{2}\) from anthropogenic emission as following equation proposed by [PERSON] et al. [17]:  
\[dXCO_{2}(grid,t)=XCO_{2}(grid,t)-MXCO_{2}(t) \tag{1}\]  
where dXCO\({}_{2}\) (grid,t) indicates the deviation from regional background for each grid at a specific time unit t where t is the 3-day unit of used mapping-XCO\({}_{2}\) data; XCO\({}_{2}\) (grid,t) is XCO\({}_{2}\) for each grid at time t from mapping-XCO\({}_{2}\) data; MXCO\({}_{2}\)(t) is median of XCO\({}_{2}\) for all grids in the study region at time  
\begin{table}
\begin{tabular}{c c c} \hline  & **ODIAC** & **CARMA** \\ \hline
**Grid, timely unit/period** & \(1^{\circ}\times 1^{\circ}\), Month/2010-2015 & Points/2009 \\ \hline
**Unit** & Ton & Ton \\ \hline \multirow{4}{*}{**Statistical sectors**} & Point sources non-point sources & \\ \cline{2-3}  & Cement production & - \\ \cline{2-3}  & Gas flaring & \\ \cline{2-3}  & International aviation and marine burker & \\ \hline \multirow{2}{*}{**Used data sources**} & Fuel statistic data published as united & The environmental protection agency and \\  & nation energy statistics database & department of energy \\ \cline{2-3}  & BP statistical review of world energy 2017 & International atomic energy agency \\ \hline \multirow{2}{*}{**Producer**} & Center for global environment research, & \\ \cline{2-3}  & national institute for environment studies & Center for Global Development \\ \hline \multicolumn{2}{c}{[PERSON] et al. [25]} & [PERSON] et al. [26] \\ \hline \end{tabular}
\end{table}
Table 1: Basic specifications of ODIAC and CARMA bottom-up CO\({}_{2}\) emission datasets.  
t calculated from mapping-XCO\({}_{2}\) data with 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) grid cell. Lastly we apply the annual mean of dXCO\({}_{2}\) (grid,t) for the year from 2010 to 2015 in the estimation of anthropogenic emission. This annual mean of dXCO\({}_{2}\) (grid,t) could detrend the seasonal variation at locale and simultaneously reduces the effect of the atmospheric transport [17].  
We computed the monthly averaged dXCO\({}_{2}\) and annual averaged dXCO\({}_{2}\) for each grid to generate monthly averaged dXCO\({}_{2}\) dataset and annual averaged dXCO\({}_{2}\) dataset from the year 2010 to 2015 with 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\) grids using the mapping XCO\({}_{2}\) dataset from 2010 to 2015. The annual dXCO\({}_{2}\) dataset and ODIAC data will be used in the following analysis.
## 4 Using the Influence Map as the State Space in Reinforcement Learning
### Combining the Original Image and Influence Map as the State Space  
Influence maps allow [PERSON] to sense danger and make smarter decisions. In Figure 4, [PERSON] moves from point A to point B. Although there are pellets ahead of his movement direction, the dangerous influence of the ghost also spreads to [PERSON]'s point B, so he chooses to change direction. Moreover, the absorbing influence of the pellets below him also spreads to point B, so [PERSON] chooses to move downward.  
Figure 3: Architecture of the Ape-x algorithm.  
When the reinforcement learning agent is trained, the agent receives the state returned by the environment and continuously learns from the interaction by understanding the information contained in the state. Therefore, the state returned by the environment directly determines the knowledge learned by the agent. Different state spaces cause the agent to prefer different policies, thereby affecting the degree of intelligence and the decision-making ability. As a high-dimensional expression, an image has considerably vast state possibilities. For example, a grayscale image with a resolution of 100 \(\times\) 100 has \(10,000^{256}\) different states, which contain large amounts of information. The more an agent can learn from such a state space, the stronger its decision-making ability.  
To learn as much knowledge as possible, the original image was used as the state space in most previous research. However, an influence map is not actually an image but a matrix. The values in the matrix represent the influence value of a certain location, not the pixel value. If the values in the matrix are scaled to the range of RGB pixel values, such as 0-255, according to certain rules, or if RGB pixel values are represented by floating-point numbers, such as 0-1, the influence map can also be regarded as an image.  
Now, the influence map can be superimposed on the original image. However, the number of pixels differs between the original image and the influence map. For the two images to be superimposed, the resolution must be the same. The original image can be changed to a tensor with a shape of (84, 84) by cropping, downsampling, grayscale, etc. The influence map can be upsampled by bilinear interpolation to (84, 84). In this way, two images with the same resolution can be superimposed on the z-axis to form a dual-channel image with a shape of (84, 84, 2). The two channels generated in this way are shown in Figure 5.  
Because the dynamic influence map has different attenuation rates in each surrounding direction, the influence can express certain motion information in an image. Compared with the traditional four consecutive frames, the influence map can also represent motion information while reducing memory usage.