esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Quality for Near-Eye Display Based on Holographic Waveguides
## 3 Results  
To study the quality of the projected image, color reproduction, and brightness non-uniformity [33; 34; 35; 36] holographic waveguides with different DOE configurations were made for a three-channel RGB system. As described above, in this implementation the holographic waveguides consist of three combined substrates for each of the RGB colors. The diffraction grating periods are different for different color channels, but the shape and size of the DOEs are the same.  
For each color channel, DOEs consisting of several diffraction gratings with the same periods but different orientation directions were manufactured. This essentially simplifies the recording setup and also makes it possible to obtain higher values of periods than, for example, in the schemes shown in Figure 2a,c, and described in [20; 30], respectively. Below is a description of the different DOE configurations tested in the experiment.
## 2 Current-Distortion Problem in Totem-Pole Bridgeless PFC Converter
### Current Distortion Caused by Input Capacitor  
Another factor that affects PF is the input filter capacitor. In PFC converters, the AC input needs an EMI filter to reduce the high-frequency switching harmonics of the inductor current. However, the input filter capacitor generates capacitive current, which leads the input voltage and degrades PF. As shown in Figure 5, the filter adopts a single-stage structure. In this figure, \(C_{1}\) and \(C_{2}\) are X capacitors, while \(C_{3}\) and \(C_{4}\) are Y capacitors. \(L_{1}\) and \(L_{2}\) are common-mode inductors, which have small leakage inductance.  
To simplify the analysis, the following assumptions are made. (a) The influence of Y capacitor is neglected since it is typically much smaller than X capacitor. (b) The equivalent input impedance of the PFC converter is \(R_{in}\). The input resistance \(R_{in}\) is calculated with the magnitude of input voltage (\(v_{in,\ m}\)) and the magnitude of the input current (\(i_{in,\ m}\)), i.e.,  
\[R_{in}=\frac{v_{in,m}}{i_{in,m}}=\frac{v_{in,m}^{2}}{2P_{o}}. \tag{5}\]  
In this case, the phase difference caused by input capacitance is calculated using  
\[\varphi=\arctan(2\pi f_{line}C_{in}R_{in}). \tag{6}\]  
The vector of the total input current is shown in Figure 6. It shows that the input filter capacitor induces a phase difference between input current and voltage. As load power decreases, the phase difference would be even more serious, which severely degrades PF.
# Spatiotemporal Dynamics of Net Primary Productivity in China's Urban Lands during 1982-2015
###### Abstract  
The rapid urbanization process has threatened the ecological environment. Net primary productivity (NPP) can effectively indicate vegetation growth status in an urban area. In this paper, we evaluated the change in NPP in China and China's urban lands and assessed the impact of temperature, precipitation, the sunshine duration, and vegetation loss due to urban expansion on NPP in China's three fast-growing urban agglomerations and their buffer zones (~5-20 km). The results indicated that the NPP in China exhibited an increasing trend. In contrast, the NPP in China's urban lands showed a decreasing trend. However, after 1997, China's increasing trend in NPP slowed (from 9.59 Tg C/yr to 8.71 Tg C/yr), while the decreasing trend in NPP in China's urban lands weakened. Moreover, we found that the NPP in the Beijing-Tianjin-Hebei urban agglomeration (BTHUA), the Yangtze River Delta urban agglomeration (YRDUA), and the Pearl River Delta urban agglomeration (PRDUA) showed a decreasing trend. The NPP in the BTHUA showed an increasing trend in the buffer zones, which was positively affected by temperature and sunshine duration. Additionally, nonsignificant vegetation loss could promote the increase of NPP. In the YRDUA, the increasing temperature was the main factor that promoted the increase of NPP. The effect of temperature on NPP could almost offset the inhibition of vegetation reduction on the increase of NPP as the buffer zone expanded. In PRDUA, sunshine duration and vegetation loss were the main factors decreasing NPP. Our results will support future urban NPP prediction and government policymaking.  
NPP; urban expansion; long time series; importance; contribution +
Footnote †: journal: Remote Sensing
## 2 Improve Based on Hss
### YOLO11 Improved Based on Hss
#### 2.2.1 Introduction of HetConv  
HetConv can handle heterogeneous and irregular input data, which traditional convolution cannot. By combining convolution kernels of different sizes (\(3\times 3\) and \(1\times 1\)), it reduces the number of floating-point operations required, thereby improving computational efficiency. Since a \(1\times 1\) convolutional kernel requires fewer parameters than a \(3\times 3\) convolutional kernel, HetConv is able to reduce the number of parameters in the model while maintaining the accuracy of the feature extraction capability.  
Figure 2 illustrates the difference between a standard convolutional filter (homogeneous) and a heterogeneous convolutional filter (HetConv): M represents the input depth (number of input channels) and P denotes the portion. Among the M kernels, M/P kernels are of size \(3\times 3\), while the rest are \(1\times 1\) kernels.  
Figure 1: HSS-YOLO structural diagram.  
Figure 2: Difference between standard convolutional filter (homogeneous) and heterogeneous convolutional filter (HetConv).  
In the P portion of the HetConv filters at layer L, the computational cost of the K \(\times\) K kernels is given by (1):  
\[FL_{k}=\frac{D\times D\times M\times N\times K\times K}{P} \tag{1}\]  
The remaining (M \(-\) M/P) kernels are of size \(1\times 1\). The computational cost of these remaining \(1\times 1\) kernels is given by (2):  
\[FL_{1}=(D_{o}\times D_{o}\times N)\times\left(M-\frac{M}{P}\right) \tag{2}\]  
The total computational cost at layer L is given by (3):  
\[FL_{HC}=FL_{k}+FL_{1} \tag{3}\]  
HetConv plays an important role in handling complex tasks and large-scale datasets. This module offers significant advantages in efficient computation, reducing computational load, and optimizing resource utilization, providing strong support for the training and inference of deep learning models.  
HetConv (heterogeneous convolution) not only reduces the number of channels in the output feature map but also simultaneously captures multi-scale features. In this experiment, we use HetConv FilterConv with p = 2, as shown in Figure 3. The computational cost of Conv is calculated by (4):  
\[FL=D\times D\times M\times N\times K\times K \tag{4}\]  
In the P portion of the HetConv filters at layer L, the computational cost of the K \(\times\) K kernels is given by (5):  
\[FL_{s}=\frac{D\times D\times M\times N\times K\times K}{P} \tag{5}\]  
HetConv can be applied to various deep learning tasks, especially when multi-scale feature capture and output channel reduction are desired, such as in lightweight network design, real-time applications, or resource-constrained environments, where HetConv may be a better choice.
## 3 Results and Discussion
To this end, 10 features from each sample test are extracted, including kurtosis, skewness, root mean square, signal-to-noise ratio, total harmonic distortion, variance, standard deviation, spurious free dynamic range, band power, and Shannon entropy. It should be noted that the training and testing data sets are selected the same as the previous sections. The training and testing phases' accuracies--83.9% and 89.3%, respectively--increased significantly when the SVM was applied to the extracted features from the time series data in comparison to the earlier technique, which involved using LSTM on the raw data. It is important to acknowledge that the hyperparameters are chosen similarly to the previous SVM network. Confusion matrices of training and testing steps on this condition are plotted in Figure 10.  
To have a clearer assessment, other defined metrics, i.e., recall, precision, and F1 score, in both input scenarios, i.e., raw signal and engineered features, are calculated from formulas 4 to 6 for the training and testing steps and presented for the LSTM and SVM models in Tables 4 and 5, respectively. Comparing the statistics of Tables 4 and 5, it can be understood that a signal processing (feature extraction) step to how extend can increase the performance of the classification model. Indeed, the applied WTS increases the dimensionality of raw signals to provide more ingredients for training the model. Additionally, research shows that using slightly more straightforward feature engineering methods and increasing the number of observations under the SVM assumption probably can also lead to acceptable results. Figure 10: Confusion matrix using the second feature extraction scenario and SVM: (**a**) training step and (**b**) testing step. Figure 9: Confusion matrix using raw signals and LSTM: (**a**) training step and (**b**) testing step.
# Evaluating the Impact of Artificial Saliva Formulations on Stainless Steel Integrity
## 3 Results and Discussion
### Structural Analysis  
Fourier-transform infrared (FTIR) spectroscopy was conducted to identify chemical changes at the surface of AISI 316L stainless steel after immersion in three artificial saliva types over 7 and 28 days (Figure 6). The transmittance spectra were compared with the initial untreated sample to assess modifications due to corrosion, adsorption, and product formation. In the case of the sample stored in Afnor (neutral pH) artificial saliva, it can be observed that the FTIR spectra show only minor changes compared to the initial 316L sample--slight increases in O-H and carboxyl bands suggest mild hydration or oxidation and indicates a stable passive film with minimal corrosion. For the sample immersed in Fletcher (basic with fluoride) artificial saliva, an increased intensity is observed in the phosphate and metal-O regions, especially at 28 days with bands around 669-718 cm\({}^{-1}\) broadened, consistent with fluoride/chloride interaction, which suggests the formation of inorganic corrosion products due to fluoride-driven surface reactions. On the other hand, the sample stored in Fusayama/Meyer (acidic) artificial saliva shows the most intense O-H and phosphate bands among all of the samples and the new peaks suggest the precipitation of calcium phosphate and significant surface alteration, which confirms severe corrosion, leaching, and chemical reactivity at the surface. The interpretation of FTIR spectra with possible assignment for each value of wavenumber is presented in Table 3.
## 5 IoRT Systems Based on Space-Borne NTN Networks
### Cost-Efficient Deployment/Operation
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Ref.** & **NTN Architecture** & **Description** \\ \hline \multirow{3}{*}{[81]} & \multirow{3}{*}{LEO} & Development of NOMA-based beamforming algorithms for tackling massive access in B5G IoT networks and minimizing total power consumption for non-critical and critical IoT applications. \\ \cline{1-1}  & & Development of the Deep Reinforcement Learning method (DRL-QoS-RA) for online joint resource allocation to solve the uplink channel allocation and power control problems for satellite IoT systems. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._uplink signals to satellites, which then transmit the data to the first gateway. However, due to the highly variable uplink channel load and the limited communication cycle of MTD, an ineffective channel reserve system results. Therefore, they propose an energy-efficient multi-access technique for M2M communication in IoRT systems and develop an energy-efficient random access protocol for delay-tolerant applications. Table 4 summarizes recent research on cost-effective deployment and operation of IoRT systems based on space-borne NTN networks. The studies proposed various solutions, including compensating for residual Doppler shifts, investigating LoRa protocol variations, using CubeSat SDN NFV-based architectures, employing SDR, and developing efficient uplink scheduling algorithms. The proposed solutions aim to minimize satellite hardware requirements, reduce gateway costs, and optimize the utilization of transmission resources, leading to more cost-efficient IoRT systems.
# Identification and Localization of Wind Turbine Blade Faults Using Deep Learning
###### Abstract  
This study addresses the challenges inherent in the maintenance and inspection of wind turbines through the application of deep learning methodologies for fault detection on Wind Turbine Blades (WTBs). Specifically, this research focuses on defect detection on the blades of small-scale WTBs due to the unavailability of commercial wind turbines. This research compared popular object localization architectures, YOLO and Mask R-CNN, to identify the most effective model to detect common WTB defects, including cracks, holes, and erosion. YOLOv9 C emerged as the most effective model, with the highest scores of mAP50 and mAP50-95 of 0.849 and 0.539, respectively. Modifications to Mask R-CNN, specifically integrating a ResNet18-FPN network, reduced computational complexity by 32 layers and achieved a mAP50 of 0.8415. The findings highlight the potential of deep learning and computer vision in improving WTB fault analysis and inspection.  
YOLO; Mask R-CNN; fault detection; wind turbine blades; inspection; deep learning +
Footnote †: journal: Applied sciences
# Reliable Augmentation and Precise Identification of EPG Waveforms Based on Multi-Criteria DCGAN
## 3 Results and Discussion
### Model Performances for Classification of EPG Waveforms  
In this study, we performed a comprehensive analysis of the performance of four models across multiple metrics (as shown in Table 4). The CNN model exhibited exceptional performance in terms of accuracy, recall, and F1 scores, nearly reaching optimal levels in all categories. In contrast, the KNN model, configured with Euclidean distance, equal weights, and a k-value of 1, showed relatively unstable performance in certain categories, suggesting a need for further feature engineering. The SVM model, using an RBF kernel function, weight equilibrium, and an auto-adjusted kernel scale, maintained a certain level of stability. However, it demonstrated insufficient recall rates in some categories, indicating the potential for further adjustments. Additionally, the DT model, with parameters including the maximum number of splits, split criterion, and minimum parent node size of 10, exhibited excellent precision but fell short in recall rates and F1 scores for certain categories. This may necessitate adjustments to the pruning operations to enhance performance.  
These results may be attributed to the following factors: firstly, external interference and insect disruptions of EPG signals, which are biological signals. Despite efforts to mitigate and minimize individual psyllid variations, data disparities persist. This leads to attribute value dissipation and consequent misclassification, particularly evident in C-waves. Secondly, inconsistent data sample quality due to the large number of data samples, resulting in varying levels of data quality. Deep learning methods are better suited to learning features under such circumstances. Additionally, slicing the waveforms results in the loss of time-series information. Ultimately, all four classifiers require more training samples to enhance their recognition capabilities.
# Detection and Localization of Overlapped Fruits Application in an Apple Harvesting Robot
###### Abstract  
For yield measurement of an apple orchard or the mechanical harvesting of apples, there needs to be accurate identification of the target apple fruit. However, in a natural scene, affected by the apple's growth posture and camera position, there are many kinds of apple images, such as overlapped apples; mutual shadows or leaves; stems; etc. It is a challenge to accurately locate overlapped apples. They will influence the positioning time and recognition efficiency and then affect the harvesting efficiency of apple-harvesting robots or the accuracy of orchard yield measurement. In response to this problem, an overlapped circle positioning method based on local maxima is proposed. First, the apple image is transformed into the Lab color space and segmented by the \(K\)-means algorithm. Second, some morphological processes, like erosion and dilation, are implemented to abstract the outline of the apples. Then image points are divided into central points; edge points; or outer points. Third, a fast algorithm is used to calculate every internal point's minimum distance from the edge. Then, the centers of the apples are obtained by finding the maxima among these distances. Last, the radii are acquired by figuring out the minimum distance between the center and the edge. Thus, positioning is achieved. Experimental results showed that this method can locate overlapped apples accurately and quickly when the apple contour was complete; and this has certain practicability.  
K-means segmentation; overlapped apples; maxima; morphological processing Article
## Nomenclature  
\begin{tabular}{l l} c & blade chord, [m] \\ CFD & computational fluid dynamic(s) \\ C\({}_{\rm d}\) & drag coefficient, [-] \\ C\({}_{\rm P}\) & pressure coefficient, [-] \\ C\({}_{\rm l}\) & lift coefficient, [-] \\ C\({}_{\rm m}\) & moment coefficient, at the mid-chord if not differently specified, [-] \\ C\({}_{\rm m/2}\) & mid-chord moment coefficient, [-] \\ f & blade oscillation frequency, [Hz] \\ k = 2\(\pi\)f c/2U\({}_{\infty}\) & reduced frequency, [Hz] \\ IBPA & interblade phase angle, [deg.] \\ PS & pressure side \\ Re = U\({}_{\infty}\)c / \(v\) & Reynolds number, [-] \\ SS & suction side \\ \(t\) & time, [s] \\ T & blade oscillation period, [s] \\ U & flow velocity, [m/s] \\ U\({}_{\infty}\) & freestream velocity, [m/s] \\ x & horizontal spacial coordinate, [m] \\ y & vertical spacial coordinate, [m] \\ \(a\) & blade angle of attack, [deg.] \\ \(\phi\) & phase angle, [deg.] \\ \(v\) & air kinematic viscosity at 20 \({}^{\circ}\)C, [m\({}^{2}\)/s] \\ \end{tabular}
# Computer Vision-Based Approach for Reading Analog Multimeter
## 1 Introduction
Based on the research mentioned above, the effectiveness of applying image differencing and binarization methods onto the pointer detection were investigated in the early stages of developing our automatic approach for reading an A-meter, where we found that the useful information lost, as well as the noise yielded, under camera/meter movement or light variation significantly affects the performance of a reading meter. Therefore, in this study, the grayscale images acquired from the A-meter (refer to the \(\mathbf{I}_{org}\), shown in Figure 2), are adopted for algorithm development. Moreover, some techniques are involved in our approach to cope with the situations just mentioned. In order to track a target under variant illumination or a moved situation, a technique to update the template could be helpful [14; 15; 16; 17]. Considering the localization capability under variant illumination and dusky environments, edge-based geometric matching and pyramidal gradient matching techniques are embedded in our approach instead of template updating to localize the wanted regions of interest (ROI) of the A-meter (refer to the main stages of _instrument meter region detection_, _angle detection of pointer_ as well as _angle detection of selector arrow_ in the flowchart of Figure 2). As a short summary, as depicted in Figure 3, a computer vision system for automatically reading an A-meter is presented in this paper, where the feasibility of our approach is demonstrated by reading the AC voltage, DC voltage, and DC current as well as the resistance value. Here, AC and DC represent \"alternating current\" and \"direct current\", respectively. Inspection by the human eye regarded as a ground truth for comparison and discussion. The rest of this paper is organized as follows. Section 2 describes the framework and system setup of our study, where the mapping relationship of the function selector and instrument meter are constructed. Moreover, two methods for locating ROIs by template matching as well as the schemes for horizontal alignment of A-meter images and locating the useful ROIs are developed. Section 3 presents the procedure for detecting the pointer's angle (or position) including the calculation of the pointer's spin center and a scanning approach to detect the pointer. An illustration is provided in Section 4 to demonstrate the functional behavior of the whole system and confirm the feasibility of our approach. The experimental results and discussion are given in Section 5. Based on a suitable modification of the proposed method, an application to monitor the storage level meter and pressure meter installed on a 15 m\({}^{3}\) liquid nitrogen (LN2) tank is demonstrated in Section 6. The conclusions of this paper and further work related to this study are finally drawn in Section 7. Figure 3: Framework of this study.
# Dual-View Integral Imaging 3D Display Based on Multiplexed Lens-Array Holographic Optical Element
## 2 Structure and Principle
### MHOE as Imaging Optical Element in the Dual-View 3D Display  
In the fabrication process, reference beams interfere with the spherical wavefront arrays. The spherical wavefront array consists of many small spherical waves, and the interference fringes are recorded on the holographic material. Each elemental lens in the MHOE is generated by the interference of a large spherical wave (reference beam) and a small spherical wave. The grating vectors of different positions on the MHOE are different. The reference beam is considered as a series of plane beams [20], whose wave vectors (\(\mathbf{K_{r1-r}}\)) are shown in Figure 4a. The spherical wavefront array can be regarded as a series of small spherical waves, which is shown in Figure 2. Each small spherical  
Figure 2: Fabrication principle of the MHOE, (**a**) the first step and (**b**) the second step.  
wave in the spherical wavefront array can also be considered as a series of plane beams, whose wave vectors (\(\mathbf{K_{s1-sn}}\)) are shown in Figure 4a. So the change of the reconstructed beam of the MHOE could be explained by the wave vector relationship of the plane beams.  
The recorded schematic diagram of the small spherical wave in the MHOE is shown in Figure 4b; the recorded grating vectors \(\mathbf{K_{g}}\) can be expressed as [20]:  
\[\mathbf{K_{g}}=\overset{\rightarrow}{x}[|\mathbf{K_{s}}|\sin[\theta_{s}]-| \mathbf{K_{r}}|\sin[\theta_{r}]]+\overset{\rightarrow}{y}[|\mathbf{K_{s}}| \cos[\theta_{s}]+|\mathbf{K_{r}}|\cos[\theta_{r}]] \tag{1}\]  
where \(\mathbf{K_{s}}\) and \(\mathbf{K_{r}}\) are the vectors of the small spherical wave in the spherical wavefront array and reference beam, respectively. In the reconstruction process, the projection beam project onto the MHOE with the vector \(\mathbf{K_{p}}\). The angle of the reconstructed beam can be expressed as [20]:  
\[\theta_{c}=\sin^{-1}\!\left[\sin[\theta_{p}]+\sin[\theta_{s}]-\sin[\theta_{r} ]\right] \tag{2}\]  
When the projection angle \(\theta_{p}\) of the projection beam, the incident angle \(\theta_{r}\) of the reference beam and the incident angle \(\theta_{s}\) of the small spherical wave incident into the MHOE are determined, the angle  
Figure 4: (**a**) Grating vector diagram of the elemental lens in the MHOE, and (**b**) wavefront recording schematic diagram of the elemental lens in the MHOE.  
Figure 3: Reconstruction principle of the MHOE.  
\(\theta_{c}\) of the reconstructed beam changes almost linearly, within the range of the projection angle of the projection beam. Therefore, the MHOE is suitable to be used as the imaging optical element in the dual-view 3D display.