esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Analysis and Laser Stripe Center Extraction for Rail Images
## 3 Experiments
### Accuracy  
In order to verify the accuracy of the detected center-points, we manually generated an image containing one light stripe. The synthetic image is 512 \(\times\) 512 pixels, and the horizontal coordinates of the ideal center line of the light stripe is 256. In order to simulate the light stripe with different width and brightness, for the first 200 rows, the width of the light stripe is set to 11, and the central lightness of the light stripe is 80. For other rows, the width of the light stripe is set to 17, and the central lightness of the light stripe is 255. Then, Gaussian noise of 0 mean and \(\sigma\) standard deviation is added to the light stripe. The synthetic image is shown in Figure 11. It should be noted that, in order to analyze sub-pixel center extraction accuracy, centerline fitting is not employed for the UM method and the proposed method.  
The detection error can be calculated as:  
\[e=\sqrt{\frac{1}{N}\sum_{k=1}^{N}(x_{k}-x_{t})^{2}}. \tag{8}\]  
Here, \(x_{k}\) and \(x_{t}\) are the detected and ideal positions of the \(k\)th line point, and \(N\) is the number of detected points. The comparison results are reported in Table 1. It can be seen that the detection error of the proposed method is the smallest. For the Steger method, its accuracy is poor when the light stripe width is varied. Both the UM and proposed methods are based on center of mass, which is more robust for the width variety of the light stripe, and the detection accuracy of our method is slightly higher than that of the UM method.
## 3 Remote Sensing of Ecosystem Health
### Remote Sensing of Vigor
#### 3.1.4 Invasive Plant Species  
Invasive plant species in diverse ecosystems can be shrubs, trees, and herb species, which alter biodiversity, structure, and function of ecosystems [89]. Identification of invasive tree and shrub species using remote sensing was successfully demonstrated [90; 91; 92; 93] using multispectral medium spatial resolution Landsat, high spatial resolution IKONOS, or hyperspectral images. Remote sensing of herb species is much more difficult and highly dependent on the separation of the species from surrounding species and background [94]. Due to the difficulty, identification of herb species was mostly conducted using hyperspectral images (reviews of [89; 95]). However, high spatial resolution IKONOS (4 m) with texture information [96] and very high spatial resolution aerial photography [94; 97] also mapped herb invaders with high accuracy. Therefore, it should be cautious to select remote sensing data with suitable spatial and spectral resolution for specific species recognition (for review see [95]). Methods used for invasive species identification mainly include visual interpretation and pixel-based and object-oriented image classification [95]. The spatial resolution issue may be overcome through the methods include SMA of one pixel [92] and combination of other ancillary data. Spectra resolution limitations may be overcome by selecting appropriate period or using time series data to maximizing difference in spectra between invasive species, native species, and backgrounds.
## 2 Data Description and Data Preprocessing
### Ratio of Peak-to-Valley (RPV)  
IPDF and PSD extract features from the amplitude and frequency domains of the raw acceleration data, respectively. However, it is important to note that these types of features emphasize the macroscopic properties of the data, reflecting overall characteristics such as acceleration amplitude symmetry and frequency components. For local data anomalies,such as local large-amplitude jumps, these features often struggle to meet application requirements. In this regard, this section proposes the RPV indicator to address local anomalies in the acceleration data.  
The calculation of the RPV indicator involves picking the maximum peak and minimum valley values of the original time series within equally spaced sliding windows and also within the peak and valley values of the time series within a constrained interval, as defined in Equation (1).  
\[RPV=\frac{|p_{r}+v_{r}|}{|p_{c}-v_{c}|} \tag{1}\]  
Figure 3: Probability density function and power spectral density of data patterns.  
where \(p_{r}\) represents the maximum peak value within the segment of the raw acceleration data, \(v_{r}\) represents the minimum valley value within the segment of the raw acceleration data, \(p_{c}\) represents the maximum peak value within the segment of the constrained acceleration data, and \(v_{c}\) represents the minimum valley value within the segment of the constrained acceleration data.  
The calculation of peak and valley values for this indicator is carried out under two conditions: one using the original acceleration data and the other using the constrained acceleration data, as illustrated in Figure 4. In this study, segment lengths of 1000 sample points are utilized, with a step size of 500 sample points. Based on the normal distribution characteristics of acceleration amplitudes, it is assumed that within the specified segment length range, normal acceleration amplitudes should be distributed within the range of \(\pm 3\) standard deviations from the mean. Values exceeding this range can be approximated as anomalous jump points. It should be noted here that to mitigate the influence of extremely small values within local segment data, the standard deviation is selected as the greater value between 0.5 and the computed standard deviation of the current segment.  
After calculating the indicator using Equation (1), a comparison is made between the data of the normal category and the outlier category. As illustrated in Figure 5, it is evident that the RPV of the normal category data does not exceed 1, while the outlier category exhibits points exceeding 1. This provides a straightforward way to visually identify abnormal patterns in the two categories of data.  
Figure 4: Schematic diagram of the RVP calculation process.
## 4 Experimental Data Acquisition  
### Experimental Testing Platform for Axial Piston Pumps  
This paper presents experimental investigations of a swash plate axial piston pump, as depicted in Figure 5a. The drive shaft is supported by front-end and rear-end bearings installed in the cover and shell, respectively. The rotor assembly comprises the cylinder block, pistons, and slippers, with the latter connected to their corresponding pistons via ball joints. The pistons, uniformly distributed around the center of the cylinder block, are essential components of the pump. The slipper pair, piston pair, and valve plate pair are three key interfaces in axial piston pumps, serving as a critical seal and support. These interfaces are crucial to the pump's lifetime and reliability.  
Figure 5b illustrates the test platform with three oil lines for the axial piston pumps: the input, leakage, and output lines. The pressure in the output line is regulated by a relief valve, and it significantly influences the pumps' efficiency and performance. Therefore, the output pressure signals of the axial piston pumps are utilized as model inputs for failure diagnosis. A pressure transducer is used to measure the output pressure, and the data  
Figure 5: Experimental testing platform for axial piston pumps. (**a**) Hydraulic pump structure diagram; (**b**) Actual operation diagram of hydraulic pump.  
acquisition module collects the pressure signals with a sampling rate set at 65,536 Hz, while the data duration is 5 s. A detailed description of the test device is presented in Table 1.  
Table 2 shows the specific models and parameters of the pressure sensors, which are mainly used to measure the flow pressure at the pump outlet.  
Table 3 displays the operating conditions of the test rig, which includes an electric motor rotating at 1500 r/min, axial piston pumps with a maximum displacement of 40 mL/r, and a pump output pressure of 21 MPa.
## 3 Types and Characteristics of Sensor Faults
### Types of Sensor Faults
#### 3.1.2 Soft Fault  
A drift fault refers to the measured value and the real value of the sensor changing with time. At this time, the output value of the sensor increases at a constant rate. Such faults must be diagnosed and restored in time, otherwise, significant measurement errors occur [47]. In addition, drift faults cause severe damage to chemical sensors and biosensors if they are not readily detected at an early stage and need to be diagnosed early [70].  
A bias fault (offset fault) refers to a constant value added to the sensor measurement value, and the sensor output result deviates from the normal value [71]. When the manipulator sensor has a bias fault, it leads to poor regulation or tracking performance and even affects the stability of the control system. In addition, faulty data may lead to wrong decisions, making unnecessary component replacement or task termination in the system [72].  
An accuracy decline fault indicates that the average value of the sensor measurement does not change, but the variance of the output value increases, resulting in a decline in measurement accuracy [74]. This fault frequently occurs in sensors, and early detection is crucial for monitoring. However, current research focuses primarily on drift faults, bias faults, and stuck faults, while accuracy decline fault research is scarce [75].  
A spike fault refers to a large amplitude spike in the measured value of the sensor, which often occurs in sensors. One of the reasons is the loose connection within the sensor node [76; 77]. When a fault occurs in sensors, the system makes wrong decisions, such as spike faults in livestock and poultry houses that exceed the set environmental threshold, causing fans, heaters, and other equipment to be turned on or off.
# Analysis of MTF in TDI-CCD Subpixel Dynamic Super-Resolution Imaging by Beam Splitter
## 4 Conclusions  
In this paper, we analyzed the MTF in TDI-CCD subpixel dynamic super-resolution imaging using a beam splitter. Firstly, we established the oversampling MTF calculation model for the imaging system using linear CCD with the subpixel-level staggered distance in the CCD array direction. Following this, the oversampling MTF calculation model for the subpixel imaging system of TDI-CCD is deduced by adding the subpixel-level staggered distance in the push-broom scanning direction. Furthermore, constraints due to the CCD sampling limit in the MTF enhancement were given. In fact, the MTF calculation model proposed in this paper can be applied to other sub-pixel imaging systems. Finally, the experimental results were relatively consistent with the theory, which proves that this theory can provide a mathematical basis for the super-resolution reconstruction process. It points to future directions for the design standards of high resolution remote sensing cameras.  
Figure 14: Details of the experiment. **(a)** Part 1 of origin image; **(b)** Part 1 of Figure 14b; **(c)** Part 1 of Figure 14c; **(d)** Part 2 of origin image; **(e)** Part 2 of Figure 14b; **(f)** Part 2 of Figure 14c.  
Figure 15: MTF comparison of the experimental results.  
**Acknowledgments:** The authors would like to thank the reviewers of this manuscript for their helpful comments and suggestions. This work was supported by the National High Technology Research and Development Program of China (863 Program, grant No. 2014 AA7026082), the Natural Science Foundation of Beijing, China (grant No. 4152045), the National Natural Science Foundation of China (grant No. 61071148) and the Science Research Project of CUC (grant No. 3132014 XNG1417, 3132016 XNG1612).  
**Author Contributions:** [PERSON] and [PERSON] proposed the model; [PERSON] and [PERSON] derived some formulas of the model; [PERSON] and [PERSON] completed some figures using a program.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 5 Experimental Results
### Experiments in Case 3: The PMSMs Have the Same Load Torque with Different Speeds  
The dc-link capacitor current is experimented for a scenario where one PMSM (PMSM-1) has a constant speed of 2000 rpm and a constant load torque of 9 Nm, and the other PMSM (PMSM-2) has a varying speed with a constant load torque of 9 Nm. The experimental results show that the dc-link capacitor current of two motor systems is lower than or equal to that of one motor system when there is a phase shift, as shown in Figure 19. The maximum dc-link capacitor current occurs at 2000 rpm, and it is 99 A-RMS when there is no phase difference between the carrier waves. If there is \(\pi/2\) rad phase difference between the carrier waves, the dc-link capacitor current is 47 A-RMS, so there is a 52.5% reduction in the dc-link capacitor current.  
Figure 17: Dc-link capacitor current for varying speeds of PMSM-1 and PMSM-2.  
Figure 18: Dc-link capacitor current for varying load torques of PMSM-2.
## 3 Human-Machine Interfaces for Data Acquisition and User Feedback
### Feedback
#### 3.2.1 Type of Feedback  
Feedback is essential in navigation tasks, especially when helping BVIP (see Table 5). A wrong choice may result in hazardous and potentially accident-prone situations for the user. The most popular choice is audio-feedback through speech instructions or sonification guidance (the use of sound or music to depict different elements present in a given scene). A tactile interface, such as a Braille display [22], or haptic systems, such as a vibrating smartphone [16; 42], are among other possibilities. The most effective solution seems to be a combination of several feedback interfaces to adapt to the different situations encountered by users. For example, audio-feedback might not be adequate in noisy environments, but the best solution when much information needs to be delivered, as in scene description tasks. A combination of feedback types were illustrated by [PERSON] et al. [46], where navigational instructions (turn left or right) are delivered by vibrating smartwatches that can also perform an audio scene description, or by [PERSON] et al. [33] with two vibrating motors on a cane for turning left and right, and an Android smartphone with text-to-audio software to provide more precise instructions.
# Creating a Regional MODIS Satellite-Driven Net Primary Production Dataset for European Forests
###### Abstract  
Net primary production (NPP) is an important ecological metric for studying forest ecosystems and their carbon sequestration, for assessing the potential supply of food or timber and quantifying the impacts of climate change on ecosystems. The global MODIS NPP dataset using the MOD17 algorithm provides valuable information for monitoring NPP at 1-km resolution. Since coarse-resolution global climate data are used, the global dataset may contain uncertainties for Europe. We used a 1-km daily gridded European climate data set with the MOD17 algorithm to create the regional NPP dataset MODIS EURO. For evaluation of this new dataset, we compare MODIS EURO with terrestrial driven NPP from analyzing and harmonizing forest inventory data (NFI) from 196,434 plots in 12 European countries as well as the global MODIS NPP dataset for the years 2000 to 2012. Comparing these three NPP datasets, we found that the global MODIS NPP dataset differs from NFI NPP by 26%, while MODIS EURO only differs by 7%. MODIS EURO also agrees with NFI NPP across scales (from continental, regional to country) and gradients (elevation, location, tree age, dominant species, etc.). The agreement is particularly good for elevation, dominantspecies or tree height. This suggests that using improved climate data allows the MOD17 algorithm to provide realistic NPP estimates for Europe. Local discrepancies between MODIS EURO and NFI NPP can be related to differences in stand density due to forest management and the national carbon estimation methods. With this study, we provide a consistent, temporally continuous and spatially explicit productivity dataset for the years 2000 to 2012 on a 1-km resolution, which can be used to assess climate change impacts on ecosystems or the potential biomass supply of the European forests for an increasing bio-based economy. MODIS EURO data are made freely available at [[ftp://palantir.boku.ac.at/Public/MODIS_EURO](ftp://palantir.boku.ac.at/Public/MODIS_EURO)]([ftp://palantir.boku.ac.at/Public/MODIS_EURO](ftp://palantir.boku.ac.at/Public/MODIS_EURO)).  
NPP; bioeconomy; forest inventory; NFI; climate; carbon; biomass; downscaling; increment; MOD17
## 2 Results and Discussion
### Optimization of the BA\(\mu\)E-\(\mu\)LD Efficiency
#### 2.2.1 Selection of Sorbent Coatings and Back-Extraction Conditions
Figure 3 present assays performed by using these six polymer-based coatings, showing the efficiency achieved for the six TCAs, under similar experimental conditions, where the best selectivity is obtained for C18 and SCN phases. Although these polymers are structurally very different, the results obtained were expected, once the C\({}_{18}\) polymer has a long aliphatic chain promoting strong hydrophobic interactions with the aliphatic chains present in the molecular structures of the target TCAs. On the other hand, the SCN polymer can promote dipole-dipole interactions through the cyano group and the electronegative elements (N and/or S) present in the chemical moieties of the six TCAs. Even so, we decided to evaluate the influence of the stripping solvent involved during the desorption step for all six polymer-based coatings. In general, the most common solvents used for the back-extraction stage in BA\(\mu\)E technique are MeOH (Figure 3a), ACN and mixtures of both (Figure 3b) [8,18]. In this sense, the data achieved showed that the use of an equivalent volume of MeOH and ACN presented the best performance, which allowed the selection of three polymeric phases, i.e., SX, C18 and SCN, the former promoting interactions through the N-vinylpyrrolidone group, demonstrating the great influence of the solvent involved during the back-extraction stage. Even so, to speed up the back-extraction process, ultrasonic treatment was also implemented by using 15, 30, and 45 min of sonification time. The results obtained (data not shown) lead us to quit using the SCN phase once it showed the worst efficiency among the other two sorbent coatings. Therefore, the SX and C18 sorbents were selected for further assays and it has been found that at least 15 and 30 min are respectively needed to fully desorb the six TCAs from the microextraction devices. Figure 2: Remaining TCAs present in the aqueous matrix after the microextraction stage using the four different ACs prepared from biomaterials waste.
# Maximization of Average Achievable Rate for NOMA-UAV Dual-User Communication System Assisted by RIS
## 4 Proposed Algorithm
### UAV Trajectory Optimization
Based on the properties of logarithms, the division of logarithms with the same base can be rewritten as the difference of two logarithms. Convert \(R_{a}[k]\) into  
\[ \begin{split} R_{a}[k]&=\log_{2}\biggl{(}1+\frac{p_ {1}[k]H_{1}[k]}{p_{2}[k]H_{1}[k]+\xi^{2}}\biggr{)}\\ &=\log_{2}\biggl{(}\frac{p_{1}[k]H_{1}[k]+p_{2}[k]H_{1}[k]+\xi^{ 2}}{p_{2}[k]H_{1}[k]+\xi^{2}}\biggr{)}\\ &=\log_{2}\biggl{(}(p_{1}[k]+p_{2}[k])+\frac{\xi^{2}}{H_{1}[k]} \biggr{)}\\ &\quad-\log_{2}\biggl{(}p_{2}[k]+\frac{\xi^{2}}{H_{1}[k]}\biggr{)}.\end{split}  \tag{42}\]  
The latter term of the above equation is expanded by the first-order Taylor expansion at the fixed point \(p_{2}^{(0)}\triangleq\{p_{2}^{(0)}[k]\},k\in\{1,2,...,K\}\), while the former term remains unchanged. We can get  
\[ \begin{split} R_{a}[k]\geq R_{a}^{\prime}[k]=&\log _{2}\biggl{(}(p_{1}[k]+p_{2}[k])+\frac{\xi^{2}}{H_{1}[k]}\biggr{)}\\ &+R_{a}^{\prime\prime}[k],\end{split}  \tag{43}\]  
we can solve \(R_{b}[k]\) directly. We can rewrite problem \(\mathrm{P}(4)\) as  
\[\mathrm{P}(5): \max_{R_{1,2}}\frac{1}{K}\sum_{k=1}^{K}(R_{a}^{\prime}[k]+R_{b}[ k]) \tag{44}\] \[s.t. \ p_{2}[k]\leq p_{1}[k],k\in\{1,2,...,K\},\] \[\ p_{2}[k]+p_{1}[k]\leq P_{max},k\in\{1,2,...,K\},\] \[\ p_{2}[k]\geq 0,k\in\{1,2,...,K\}.\]  
Since problem \(\mathrm{P}(5)\) features a linear objective function coupled with linear inequality constraints, its convexity can be easily proven. Thus, the transformed problem \(\mathrm{P}(5)\) can be solved by using the CVX [29] tool.
## 3 Simulation Analysis
### Influence of the Exciting Voltage  
To investigate the sensitivity of dynamic response characteristic of VCM-DHPV to the different exciting voltage, the simulations are performed at the exciting voltage of 150, 180, 240 and 280 V (with the supply pressure of 8 MPa). It can be seen in Figure 6 that the opening response time of VCM-DHPV decreases while the exciting voltage \(u\) gets bigger. This is because the bigger exciting voltage of the VCM coil could rapidly increase the current. Thus, the opening response time of the VCM becomes shorter. The opening response times of the on/off valve at the exciting voltage \(u\) of 150, 180, 240 and 280 V are 9.1, 8.2, 6.8 and 6.2 ms, respectively. Therefore, from the increase of the dynamic characteristic purpose point of view, the exciting voltage should be appropriately improved during the process of switching for VCM-DHPV.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Description** & **Notation** & **Value** & **Unit** \\ \hline Mass of coil and spool & \(m\) & 0.28 & kg \\ Maximum stroke of the spool & \(x_{\text{max}}\) & 1.0 & mm \\ Nominal mass flow rate & \(q_{\text{turn}}\) & 0.5 & g/s \\ Gain of VCM & \(K_{e}\) & 44.2 & N/A \\ Maximum coil current & \(I\) & 8.4 & A \\ Coil resistance & \(R\) & 4.6 & \(\Omega\) \\ Coil inductance & \(L_{s}\) & 220 & mH/kHz \\ Viscous friction coefficient & \(k_{f}\) & \(8\times 10^{-3}\) & N/(m/s) \\ Spring stiffness & \(k_{s}\) & 0.02 & N/m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design parameters of VCM-DHPV.  
Figure 5: AMESim model of VCM-DHPV.  
Figure 6: Dynamic response curves under different exciting voltage (\(P_{s}\) = 8 MPa, \(\kappa\) = 45\({}^{\circ}\)).
# New Approach for Brain Tumor Segmentation Based on Gabor Convolution and Attention Mechanism
## 1 Introduction
The input image is processed with Gabor filtering and the obtained information is combined with the original convolution result to extract more accurate features for brain tumors, as well as edema regions and other related details, especially fine tumors. In addition, the loss of fine local features in an image due to multiple convolutions can be substantially reduced. The 3D CBAM attention mechanism and the subsequent down-sampling operations are finally utilized to generate segmentation results. Experimental results show that the proposed method can achieve improved segmentation accuracy and outperform a few state-of-the-art methods for the segmentation of tumor images. The major contributions of the paper can be summarized as follows. 1. A new deep learning model that incorporates both the Gabor convolution and 3D CBAM attention mechanism into the 3D U-Net architecture is proposed for the segmentation of MRI images of brain tumors. 2. Experiments have been performed to evaluate the performance of the proposed approach. Its segmentation accuracy is compared with that of several other state-of-the-art methods for brain tumor segmentation.
# Feedback-Forward Neural Networks for Failure Mechanics Problems
## 5 Neural Network (NN)-Based Phase-Field Brittle Fracture
### Feed-Forward Neural Network  
A feed-forward neural network with an architecture of 8-15-15-1 is applied to learn the relationship between input and output datasets. The input layer of the neural network contains 8 neurons (3 strains, 3 stresses, 1 old phase-field and 1 driving force), and the two hidden layers containing 15 neurons each. The output layer contains one neuron which predicts the fracture phase-field \(d\). The Levenberg-Marquardt algorithm [54] is applied as an optimizer for the training of the neural network. The rest of the NN structure follows the same procedures and techniques described in Section 2. After this training, a performance in the order of \(10^{-5}\) is achieved, as demonstrated in Table 7.