esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Simulation Model-Driven Fault Diagnosis Method for Bearing under Missing Fault-Type Samples
## 3 System Framework and Model Training
### Model Training Procedure WGAN-GN-SAE  
1. The system dynamics model of rolling bearing is established to perform the bearing fault modeling, and the missing fault simulation vibration signal of rolling bearing is obtained.
2. The signal is pre-processed by fast Fourier transform (FFT) and Hilbert transform to acquire the envelope signal, then the training and testing data are equally separated.
3. The training data is input into WGAN-GN for data enhancement.
4. The simulated data generated by WGAN-GN are coupled with the original data to enhance the dataset and form a complete fault dataset.
5. The complete fault dataset is used as training data of the SAE network, and the testing data are used for model testing.
## 4 Evaluation
### Optimal Design of Sensor Heating Layer
In terms of heating strategy, the traditional approach sets the target temperature at 20 \({}^{\circ}\)C regardless of the sensor's environment. In the case of limited energy in the detection system, this solution dramatically increases the power supply pressure of the system. For the humidity sensor in the atmospheric detection system, the design of the heating electrode is mainly to solve the influence of high altitude and low temperature. The condensation phenomenon is caused by the temperature of the sensor being lower than the ambient temperature; so, in the design, it is only necessary to make the temperature of the sensor slightly higher than the ambient temperature while satisfying the regular operation of the sensor. To reduce the useless power loss of the system, we set the temperature of the electrode to be only 10 \({}^{\circ}\)C higher than the ambient temperature each time. This real-time closed-loop heating control strategy guarantees the sensor's performance, saving system energy. Therefore, in the case of the same system input voltage, appropriately increasing the resistance of the heating electrode can meet the heating requirements of the sensor. Based on the above analysis, we designed a new heater structure with full consideration of each system's heat transfer and power consumption. It not only ensures the uniformity of temperature changes in all directions but also reduces the useless power loss by reducing the cross-sectional area of the resistance and expanding the heat transfer area. Using the same initial setting conditions, we conduct a heat transfer model analysis on the newly designed heating structure, and the results are shown in Figure 14. Through calculation, the optimal heating area of this structure is 6.81 mm\({}^{2}\), and the lateral and longitudinal temperature distribution curves are relatively gentle, which meets the design requirements of heat transfer uniformity and is superior to other traditional electrode structures. In terms of the heating strategy, the closed-loop control reduces the steady-state temperature required for each stage of the system so that the heating layer can reach the preset temperature faster. It not only reduces the warm-up time and power used by the sensor but also dramatically reduces the useless power loss of the system. Figure 14: (a) Thermal analysis of new structure. (b) Lateral temperature distribution curve. (c) Longitudinal temperature distribution curve.
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 4 Workflow Stages in MLOps
### Data Management Phase  
As project-specific data may be restricted by domain-dependent constants, the relational integrity of attributes, the validity of historical timelines or state-dependent transitions [34], archiving overall data quality is not an easy task. Furthermore, the life cycle of data sets is associated with different quality dimensions, as described in [23]. Data quality in ML is closely related to principles of 'big data'. Quality issues should be discovered as early as possible in order to isolate and adapt faulty processes which impact subsequent utilization [17]. Subject-specific experts (e.g., Domain experts) are responsible for providing problem specific questions and goals, as well as KPIs for (data) models to create. The validation of potential (data as well as ML) models is in their area of responsibility, too [30]. As data scientists build operational ML models which address the formulated problem, the scientific quality must be assessed in tandem with the domain experts [30]. Depending on the organization's structure, there are additional involved entities and roles for data operations, for example, the Data Stewardship [35], which is responsible for supervising data during various phases of a project. With a focus on Data Quality Management (DQM) and data governance, multiple data steward-related roles were defined in early approaches in [36], distinguishing between chief, business, and technical data steward, as well as a data quality board which defines the data governance framework of the organization. Especially when planning or finishing the implementation of a project, this entity is responsible for maintaining resulting data, often represented by a Data Management Plan (DMP) [37]. In [38], the stewardship was divided into three roles, where data stewards provide guidance on data governance (e.g., data integrity, provenance, requirements, and improving quality metadata), domain knowledge, and scientific integrity (e.g., data quality and usability evaluation) is provided by scientific stewards and software, and system guidance (persisting and accessing data) is provided by technical stewards. As there is a variety of tools for supporting data stewards, as well as every other involved individual, in their domain-specific work, the best practice is to gather all tooling for each experiment in a repository, accessible by all team members [39]. Concerning the quality of actual processing data, the FAIR data principles are often quoted [39] in the context of data stewards. The definition of principles for increasing reusability of data sets describes characteristics for systems with a focus on valuable research output [40]. Even when only publishing these data sets within a project, the complete data life cycle may profit from such FAIR-implementing products, such as in [37].
## 4 Results and Discussion
### Correlation of Bio-Indicators with NEE  
The bio-indicators have significant correlations with GPP and Re, however we did not find significant correlation relationships between all bio-indicators and NEE. The non-significant correlations were a result of two factors. NEE showed a large variability during short temporal periods of several days in contrast to GPP and Re (Figure 6). Bio-indicators can only evaluate the condition of the forest canopy during the time periods of the satellite overpasses. This means that bio-indicators cannot be used to accurately evaluate the daily variation of NEE. A second reason for the observed significant correlations  
Figure 11: Statistical histogram of GPP frequency distribution between different models **(a)** SPRI*NDVI, **(b)** VPM, **(c)** BEPS, **(d)** MOD17A2 in the same time period.  
was that in the winter season the snow below the forest canopy has an influence on the canopy reflectance in VNIR. This resulted in low correlations of bio-indicators for estimating NEE in the winter.  
To overcome these sources of error, we removed the Hyperion images acquired in winter from our model validation. Ten growing season images were used to analyze the correlation between bio-indicators and NEE (Table 1). The average NEE of 5 days (NEE5) was used to determine the correlations. Eight bio-indicators had significant correlations with NEE5 (Table 5). Among the seven bio-indicators, NDNI correlated with NEE5 (R = \(-\)0.643, P = 0.045) and had a significant correlation with NEE (R = \(-\)0.805, P = 0.0050) (Figure 12). This result suggests that narrowband vegetation index with additional SWIR information might be useful to monitor the variation of forest ecosystem NEE since canopy nitrogen has been shown to promote NEE.
# Dynamic Changes in and Driving Factors of Soil Organic Carbon in China from 2001 to 2020
## 2 Materials and Methods
### Changes in SOCD Trends and Attribution Analysis
#### 2.2.1 Land Use Change Analysis and Intensity Detection  
The land use data were MODIS land cover products (MCD12Q1, V051) for the years 2001, 2005, 2010, 2015, and 2020 with a spatial resolution of 500 m downloaded from the LAADS DAAC platform ([[https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)]([https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)), URL (accessed on 15 October 2023)). The land use transfer matrix is an analytical method that describes the mutual shifts between different land classes. In this study, to facilitate the analysis, the land use data were reclassified into six categories (forestland, grassland, cultivated land, built-upland, barren land, and other) using ArcGIS 10.8 (Esri, Redlands, CA, USA) software, and then land use transfer matrix analysis was performed for the years 2001-2020.  
The extent of land use change was detected using a change vector analysis. CVA is a change detection method for preclassification detection, where the image is subjected to rigorous pre-processing (radiometric correction, geometric alignment) before change detection [36; 37]. As shown in the following equations, the extent of change is indicated by the magnitude of the vectors between the two temporal phases, and the changed/unchanged region between the two temporal phases is determined [38; 39]. This study used MODIS bands to detect the intensity of land use change in the years 2001-2005, 2005-2010, 2010-2015, and 2015-2020.  
\[\Delta v_{i}=r_{i}-s_{i} \tag{5}\]  
\(\Delta v\) is the variation vector, \(r\) is the time-phase one image, and \(s\) is the time-phase two image.
# Compressive Sensing Imaging Based on Modulation of Atmospheric Scattering Medium
## 4 Experiment Results  
For further validation, the CS imaging experiment was carried out. The schematic diagram of the experimental setup is shown in Figure 6. The light source was a laser with a wavelength of 532 nm (ADR-1805, Changchun Laser Photonics Technology Co., Ltd., Changchun, China). A collimator (GCO-25, Daheng New Era Technology Co., Ltd., Beijing, China) enlarged the laser beam by a factor of 10 and projected it onto a DMD (DLP F6500, Jinfei Photoelectric Technology Co., Ltd., Henan, China). The DMD had more than 2 million micromirrors with a resolution of up to \(1920\times 1080\). The atmospheric modulation patterns with \(32\times 32\) units were generated by MC simulation and loaded onto the DMD, so the unit of the atmospheric modulation patterns displayed onto the DMD were composed of \(32\times 32\) micromirrors. The modulated light illuminated the target and was then reflected. An optical collecting system formed by a lens group focused the light within the sensitive area of a photomultiplier tube (PMT) (H10722-20, Hamamatsu Photonics Co., Ltd., Hamamatsu City, Japan). The detected signal from the PMT was digitized by an analog-to-digital converter (DAQ-2213, Adlink Technology Co., Ltd., New Taipei City, Taiwan). A program written in C++ was customized to control the measurement process.  
Using the measured values obtained by the PMT, the OMP algorithm reconstructed the image. The experimental results are shown in Figure 7. The top row shows the reconstructed images and the bottom row shows the 3D displays of the images. The quality of the experimental results was not quite as good as the simulation results. One possible reason is that, after beam expansion, the laser light source was not uniformly collimated light, but a Gauss-like light source with a strong middle and a weak surrounding. Thus, the intensity of the reconstructed image around the middle intersection is relatively high. Nevertheless, the characteristics of the target can be seen clearly. The experimental results also proved that the atmospheric scattering media can be used as a DMD for CS imaging.  
Figure 6: Schematic diagram of the experimental setup. A 532 nm laser beam passed through an expanding system which consisted of lenses L1 and L2, and then was reflected by a digital micromirror device (DMD). The patterns loaded onto the DMD were an atmospheric modulation matrix simulated by the Monte Carlo (MC) simulation. A lens, L3, was placed between the DMD and the target to image the modulation patterns on the target. The reflected light from the target reached the photomultiplier tube after being focused by the optical collecting system, L4, and filtered by the spectral sheet, F. The detection signal from the photo multiplier tube (PMT) was used for image reconstruction.
# Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG-VSC Algorithm
## 4 Flight Validation and Performance Analysis
### Flight Validation and Performance Analysis  
The validation of the MADDPG-VSC algorithm's performance is demonstrated through real-world formation flight experiments. The validation process emphasizes the algorithm's effectiveness in achieving cooperative control, obstacle avoidance, and overall UAV coordination. Key performance metrics, including trajectory tracking, formation stability, obstacle avoidance, and real-time responsiveness, are analyzed and discussed.
# Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning
## 3 Methodology
### Vitis AI Deploys CNN
#### 3.4.3 DPU Core Configuration  
A deep learning processor (DPU) is a programmable accelerator optimized for neural network models. It consists of a set of parameterizable IP cores that can be implemented through hardware programming without layout and wiring. DPU supports most operations of deep learning, such as convolution, pooling, full connection, activation, etc. Vitis AI provides a series of DPUs for Xilinx's Kria KV260, Versal card, Alveo card, Zynq UltraScale + MPSoC and other embedded devices. The parameters of the DPU can be configured according to the application, so as to improve throughput, latency, and scalability, and unique differences and flexibility are achieved in terms of power consumption. The DPU architecture parameters are shown in Table 1. Combining the logic resources of KV260 and the parameters of the network model, we choose B4096 as the core of the DPU architecture. The DPU 4096 architecture provides high computational power and processing speed, making it suitable for handling complex tasks and large-scale data. The KV260 faces high demands for computational resources, requiring the processing of massive amounts of data and complex calculations. The high-performance computing capability of the DPU 4096 makes it an ideal choice to meet these requirements. Additionally, the DPU 4096 architecture has been widely applied and validated, ensuring its stability and reliability. Choosing a proven architecture like this helps mitigate risks during development and deployment and ensures system stability and consistency.
# A Low-Loss Circuit with High-Pass Low-Pass Broadband Flat Negative Group Delay Characteristics
## 2 Circuit Design and Analysis with High-Pass and Low-Pass Negative Group Delay  
As shown in Figure 1, the circuit that can achieve high-pass and low-pass negative group delays is composed of a branch connected in series with a capacitor \(C_{1}\) and in parallel with an inductor \(L_{1}\), resistor \(R_{1}\), and inductor \(L_{2}\), and then connected in series with a branch connected in parallel with a resistor \(R_{2}\) and inductor \(L_{3}\). The topology retains the structural characteristics of both the high-pass prototype and the low-pass prototype, and the circuit can be switched between low pass and high pass by adjusting the parameters of the components in the circuit. By changing the values of capacitors \(C_{1}\), inductors \(L_{3}\), \(L_{2}\), and resistors \(R_{1}\), \(R_{2}\), the circuit can have the function of a high-pass or low-pass flat negative group delay circuit. Compared with previously designed negative group delay circuits, the negative group delay circuit designed in this work can achieve high-pass negative group delay while maintaining fairly high flatness.  
The impedance value of the circuit is given in Equation (1), and the equivalent [\(ABCD\)] matrix model is shown in Figure 2. The [\(ABCD\)] matrix value of the circuit is also given in Equation (2). Moreover, the partial admittance of the circuit is given in Equations (3) and (4). By synthesizing Equations (2)-(4), the transfer function of the circuit can be obtained, as given in Equations (5) and (6), and the group delay is given in Equation (7). The results of achieving high-pass and low-pass negative group delays in the circuit are shown in Figures 3 and 4.  
Figure 1: Negative group delay circuit structure diagram.  
\[Z=R_{1}+j\omega L_{2}-\frac{1}{\frac{j}{L_{3}\omega}-\frac{1}{R_{2}}}+\frac{1}{j \omega C_{1}-\frac{j}{L_{1}\omega}} \tag{1}\]  
\[\begin{bmatrix}A&B\\ C&D\end{bmatrix}=\begin{bmatrix}1&\frac{1}{\frac{1}{Z_{2}}}\\ Y_{1}&1+\frac{Y_{1}}{Y_{2}}\end{bmatrix} \tag{2}\]  
\[Y_{1}=\frac{1}{R_{1}+j\omega L_{2}+\frac{1}{j\omega C_{1}-\frac{j}{\omega L_{1} }}} \tag{3}\]  
\[Y_{2}=\frac{1}{R_{2}}-\frac{j}{\omega L_{3}} \tag{4}\]  
\[S_{21}=\frac{2}{A+\frac{B}{Z_{0}}+CZ_{0}+D} \tag{5}\]  
\[S_{21}=\frac{-2}{E+F-G-2} \tag{6}\]  
\[\left\{\begin{array}{l}E=\frac{Z_{0}\left(C_{1}L_{1}\omega^{2}-1\right)}{R_ {1}-C_{1}L_{1}R_{1}\omega^{2}+L_{1}\omega+L_{2}\omega-C_{1}L_{1}L_{2}\omega^{ 3}\right\}\\ F=\frac{L_{1}R_{2}\omega}{2\omega(-L_{3}\omega+R_{2})}\frac{L_{3}R_{2}\omega (C_{1}L_{1}\omega^{2}-1)}{(-L_{3}\omega+R_{2})(R_{1}-C_{1}L_{1}R_{1}\omega^{2} +L_{1}\omega j+L_{2}\omega j-C_{1}L_{1}L_{2}\omega^{3}j)}\\ \\ \tau(\omega)=-\frac{\partial Z_{21}}{\partial\omega}\\ \end{array}\right. \tag{7}\]  
Figure 3: The group delay result generated by the circuit shown in Figure 1.  
Figure 2: Equivalent [ABCD] matrix model.
###### Abstract
They verified that the deposition, penetration of droplets, and drift distribution were affected by the droplet size. [PERSON] et al. [34] used machine learning methods to analyze the droplet diameter and distribution pattern of the nozzles. Among the various nozzles, flat fan nozzles are widely used for unmanned aerial sprayers [35] because of a high spray efficiency [36]. In contrast to flat fan nozzles, AI nozzles produce large droplets by including air bubbles inside the droplets and reduce the drift of droplets [37; 38; 39]. [PERSON] et al. [40] developed a new AI nozzle for aerial spraying, tested it, and found that it worked as designed and had a smaller spray diameter than a conventional AI nozzle. However, there is a lack of research that actually uses drones equipped with AI nozzles to conduct control and analyze their effectiveness. Therefore, research is needed to actually perform aerial spraying with AI nozzles and compare performance differences with conventional nozzles when using drones. When comparing the performance of two nozzles, the effect of pest reduction when pest control is conducted using agricultural drones must also be investigated [41]. The control efficacy is one of the most important criteria of the spraying system. However, most previous studies have only examined the deposition of spray droplets using collectors to evaluate the physical spraying performance of agricultural drones. Furthermore, even when pest control is conducted using the same system, the penetration rate of plant protection products may vary depending on the size and shape of the crops, affecting pest control efficacy [25; 42; 43]. However, only a few studies have been conducted to compare the differences in the pest control efficacy of crops when using agricultural drones. The main objective of this study was to compare the spraying performance of a flat fan nozzle and an AI nozzle when using a drone. To compare spraying performance, the deposition, penetration rate, and control efficacy have to be analyzed using water-sensitive paper. In order to compare the different results between crops, we planned to perform the experiment on soybeans and rice. Crops have to be collected after pest control to compare control efficacy by investigating the yield and damage caused by pests.
# Hypersensitivity in Molar Incisor Hypomineralization: Superficial Infiltration Treatment
## 1 Introduction  
In 2001, [PERSON] proposed the term molar incisor hypomineralization (MIH) to define a qualitative defect of the enamel from a systemic origin, which can affect 1 to all 4 of the permanent molars and can also involve the permanent incisors [1].  
The prevalence of different MIH defects varies according to geographical areas, from 2.4% to 44% [2; 3; 4]. Clinically, tooth enamel affected by MIH is hypomineralized and opaque with a porous appearance and it is either white, yellow or brown in color [5; 6; 7].  
In studies conducted with a polarized light microscope [8; 9; 10; 11], the enamel defect appeared so porous that immediately after tooth eruption fractures or cracks under the action of masticatory forces (post-eruptive enamel breakdown-PEB) could occur [12].  
The different composition of the enamel affected by MIH and its altered crystalline structure are the cause of the main clinical issues reported by the affected patients. The high hypersensitivity of these elements is therefore the reason for the poor quality of life during the daily oral maintenance procedures and during the consumption of food and cold drinks.  
The treatment of teeth affected by MIH should be a minimally invasive procedure that aims to protect, strengthen and preserve dental structure. Numerous therapeuticalternatives have been proposed over time for the treatment of MIH-affected teeth [13,14], but the clinical management of these conditions is very demanding and difficult to learn for generic dentists, who often adopt less conservative treatments [15].  
The scientific community exclusively proposes non-specific treatments based on fluoride and/or casein mineralizers or desensitizing pastes, in order to reduce hypersensitivity [16,17]. However, there are no studies available defining guidelines for this condition.  
The aim of this study is to propose a precise and standardized protocol for the improvement of hypersensitivity in patients with MIH through a superficial resin infiltration technique.