esa-sceva/satcom-chunk-collection huggingface dataset:
# Feature Extraction of Bearing Weak Fault Based on Sparse Coding Theory and Adaptive EWT
## 1 Introduction
[20] proposed a method base on EWT and duffing oscillator to diagnose bearing compound faults and proved the application performance of EWT is better than EMD series methods. By analyzing the dictionary to generate the overcomplete dictionary required by the sparse algorithm, it is usually necessary to determine the optimization range or exact value of the relevant parameter. In this paper, a sparse dictionary is constructed using a Laplace wavelet, the morphological parameters of which have been a subject of intense research interest. For traditional deterministic morphological parameters methods such as correlation filtering, it is impossible to obtain the damping ratio and resonance frequency parameters when processing low SNR signals. At the same time, if the parameter optimization range is too large, the scale of the dictionary will be extremely large, which can result in extremely low computing efficiency. It can be seen that the superiority of EWT is demonstrated through the analysis of related methods. However, EWT also has challenges, such as over-decomposition inhibition and optimal component selection. Therefore, a feature extraction method of bearing weak faults based on sparse coding theory and adaptive EWT is proposed in this paper. Firstly, the sparse coding theory is briefly described, and the parameter determination method of a Laplace wavelet dictionary combined with the EWT method is provided. Then, the AEWT based on a scale space method is briefly described, and the component selection method is given. Next, the algorithm flow is presented, and the simulation signal and bearing life test data are analyzed. Finally, the proposed method is compared with FSK, CF, SISC based on a learning dictionary, BPDN based on a wavelet packet and the WPT Kurtogram. The results of this comparison verify the superiority of the proposed method.
## 4 Overview of GNSSs
### Quasi-Zenith Satellite System  
The Quasi-Zenith Satellite System (QZSS) is a regional satellite navigation system implemented by the Japanese government since 2003. It was originally intended to augment the US GPS in Japan with three satellites. The satellites have an orbital period of 23 h 56 min, a higher orbit inclination, and low eccentricity, which optimizes visibility at high elevation angles in Japan. This is crucial for urban and mountainous areas where GPS signals are insufficient. Each of the three IGSO satellites flies over Japan for eight hours, ensuring continuous availability of navigation signals.  
The first QZSS satellite, QZS-1, was launched on 11 September 2010. After successful testing, on 30 September 2011, the Japanese government announced the establishment of a constellation of four QZSS satellites as national infrastructure by the end of 2010, with a goal of expanding to seven satellites by 2023. The National Space Policy Secretariat (NSPS) in the Cabinet Office is responsible for implementing the system. In 2013, a tender was launched to establish a ground control segment and provide services from 2018 to 2033 under the Private Finance Initiative (PFI) project [92].
# Quasi-All-Passive Thermal Control System Design and On-Orbit Validation of Luojia 1-01 Satellite
###### Abstract  
In order to resolve the large fluctuations in temperature range problem of Luojia 1-01 satellite caused by low heat inertia and poor thermal conductivity of structure, a quasi-all-passive thermal control system (TCS) design is presented under the conditions of limited resources including mass and power consumption. The effectiveness of the TCS design is verified by both ground thermal balanced test and related telemetry data of on-orbit performance. Firstly, according to the structural features and working modes of the satellite, isothermal design was implemented and the effectiveness was verified by thermal analysis using finite element method. Secondly, based on the results of the thermal analysis, thermal design was optimized and verified by the thermal balanced test. Finally, the thermal design was proved to be effective by temperature data acquired from telemetry data of on-orbit performance, and the thermal analysis model was improved and updated based on the results of thermal balanced test and temperature data of on-orbit performance. The on-orbit data indicates that temperature of optical camera stables at about 12 \({}^{\circ}\)C, temperature of battery stables at 19 \({}^{\circ}\)C, temperature of instruments inside and outside the satellite cabin is ranging from 10 \({}^{\circ}\)C to 25 \({}^{\circ}\)C. Temperature fluctuation range of optical camera is less than 2 \({}^{\circ}\)C when it is not imaging. Temperature fluctuation range of instruments not facing the sun is less than 4 \({}^{\circ}\)C. The data suggests that the temperature level of the satellite meets general design requirements, and the quasi-all-passive TCS design of the satellite is practicable.  
quasi-all-passive; thermal analysis; thermal balanced test; on-orbit performance; temperature fluctuation +
Footnote †: journal: Physics Letters B
## 3 Results
### Global Continuous TanSIF Product  
Based on the RF model, a global, continuous SIF dataset that included cos (SZA\({}_{0}\)) at the transit time for the central latitude and longitude of each grid cell, normalized to the same level (0.2\({}^{\circ}\)\(\times\) 0.2\({}^{\circ}\)) as TROPOMI for the period 2017-2019 was produced. This dataset had a spatial resolution of 0.05\({}^{\circ}\) and a temporal resolution of four days. Figure 7 shows the spatially continuous TanSat SIF products for July and December in 2018, together with the NDVI for comparison. Tropical rainforest areas had high SIF values in both July and December. In July, southeast and northeast China, the eastern United States, and southern Europe as well as southern Russia were hot spots of SIF, which agrees well with the distribution of dense vegetation.  
Since the transit time of TanSat and TROPOMI was almost the same at about 13:00 local time, the continuous TanSat SIF data were also validated using TROPOMI SIF data. In order to reduce the impact of noise, 0.05-degree TanSat SIF was aggregated to 0.2-degrees so that it could be compared with 0.2-degree TROPOMI SIF, as produced by [PERSON] et al. (2018). The compar  
Figure 6: Performance of RF for TanSat SIF normalized by cos (SZA) using band reflectances, air temperature and cos (SZA\({}_{0}\)) and validation data sets from 2017–2019. The three columns show the validation results for each year. The different colors represent the sample density. The diagonal black line depicts the 1:1 relationship. The units of RMSE are mW m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\).  
Figure 7: Global continuous TanSat SIF and NDVI for July and December 2018. The units of SIF are mW m\({}^{-2}\) μm\({}^{-1}\) sr\({}^{-1}\).  
Figure 8. Since TROPOMI SIF is retrieved at around 740 nm, it has a higher value than TanSat SIF, which is retrieved at 758 nm. R\({}^{2}\) was used to evaluate the consistency between the two data sets. The results show that the continuous TanSat SIF dataset agreed well with the TROPOMI SIF with an R\({}^{2}\) of 0.73, indicating that the SIF can be well modeled by integrating cos (SZA\({}_{0}\)) with other metrics.  
The values of TanSat SIF were all lower than 2.5 mW m\({}^{-2}\)\(\upmu\)m\({}^{-1}\), whereas the maximum value of TROPOMI SIF reached around 6 mW m\({}^{-2}\)\(\upmu\)m\({}^{-1}\). Compared with TROPOMI SIF at 740 nm, TanSat SIF at 758 nm was obviously lower in the high (\(>\)1 mW m\({}^{-2}\)\(\upmu\)m\({}^{-1}\)) fluorescence region but higher where the fluorescence was around 0.1-0.5 mW m\({}^{-2}\)\(\upmu\)m\({}^{-1}\).
# The Influence of Ionizing Radiation on Paclitaxel-Loaded Nanoparticles Based on PLGA
## 2 Materials and Methods
### Biological Assay  
The cytotoxicity of the polymer was evaluated according to the ISO 10993-5:2009 guideline [20], i.e., by the neutral red uptake (NRU) test using BALB/c 3T3 clone A31 mice fibroblasts cell line (American Type Culture Collection). The BALB/3T3 clone A31 line used in the assay is recommended by ISO experts for the NRU cytotoxicity assessment test [20]. The polymeric extracts were prepared by incubating the samples in 1 mg mL\({}^{-1}\) DMEM medium with 10% bovine serum (24 h, 37 \({}^{\circ}\)C) with stirring. The extracts were sterilized using a syringe filter. Highly cytotoxic latex and non-cytotoxic polyethylene were used as reference materials. The samples were considered cytotoxic if they reduced cell survival below 70% when compared to the untreated cells.  
The genotoxicity of the polymeric materials was evaluated according to ISO 13829:2000 guideline [21], i.e., by the umu-test using _Salmonella typhimurium_ TA3515/psk1002 (Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH, Germany). The principle of genotoxicity assessment by the umu-test is based on the use of the genetically engineered _Salmonella typhimurium_ strain TA3515/psk1002, carrying the umuC-lacZ fusion plasmid. When the SOS response is induced by genotoxins, the umuC-lacZ fusion gene is expressed, and the umuC-lacZ fusion product protein is induced. Since this protein has \(\beta\)-galactosidase activity, it is possible to check the inductivity of umuC gene expression by measuring its activity colorimetrically. The experiment was conducted with and without metabolic activation (S9 rat liver fraction). The polymeric samples were incubated in PBS buffer (GIBCO) for 24 h at 37 \({}^{\circ}\)C. The extracts were sterilized by filtration before the assay. The 2-aminoanthracene and 4-nitroquinoline N-oxide were used as positive controls. The genotoxic potential of the samples was determined by Induction Ratio (_IR_). Samples were considered genotoxic when the _IR_ value was min. 1.5.
## 3 Results and Discussion
### The Impact of FS Blending on the Membrane Properties
The results also indicate that different contents of FS resulted in variable pore sizes, where the PVDF-FS4 and the PVDF-FS7 resembled the lowest and the largest pore sizes, respectively. As shown in Figure 4b, out of all membranes, the PVDF-FS4 has the smallest mean pore size whilst the PVDF-FS7 has the largest mean pore size. This can be attributed to the larger interlinking effect that occurs within the skin layer of the polymer when specific amounts of FS particles are added. Another important property of membranes utilized in solar-assisted desalination applications is their ability to absorb solar irradiation and its subsequent conversion into thermal energy through conduction and convection of the water within the membrane pores. We performed solar absorbance (Figure 4c) and thermal conductivity measurements (not shown) on our membrane sheets, and the results suggest that all membranes exhibited similar solar absorbance over 400-800 nm wavelength, all of which were slightly higher than that of the PVDF membrane. Moreover, all membranes exhibited similar thermal conductivity (0.7 W mK-1 \(\pm\) 0.1), indicating that the effect of varying the FS amount on the heat transfer properties of our as-fabricated membranes is small, and providing supporting information that the performance of our as-fabricated membranes likely performs similarly in terms of heat transfer and solar absorbance.
## 4 Parallel Design and Implementation of DMAS Algorithm
### Parallel Design of DMAS Using Spark  
The multiplication and summation steps of the DMAS are well suited for map and reduce operations. The steps of DMAS based on the MapReduce paradigm and Spark framework are as follows:  
1. Data Parallelism: Retrieve and store the sensor data. Let the number of antennas used for data collection be \(M\); an array of the same size in monostatic mode or \(M(M-1)/2\) size in multistatic mode needs to be created. Use the Spark context object to parallelise the data and to convert it to RDDs. Distribute these RDDs to the worker nodes. This also includes broadcasting an empty matrix to store pixel values to each worker node. These RDDs are input to the next step.
2. Map Operations: Since pixel values are calculated using the same equation (Equation (4) in this work), they can be calculated in parallel. The Map operation is used to multiply the signals in pairs and to sum it up to calculate pixel values.
3. Reduce Operations: Integrate the subsets of pixel values using the \"reduce\" operation.
4. Iterate: Update the empty matrix of pixel values according to the values obtained from the \"reduce\" operation. Iterate the map and reduce tasks until all signals are processed.
## 4 Case Study and Results
### Energy Consumption Results
#### 4.2.1 Scenario 1--Ideal Flight  
Figure 3 shows the total energy consumption for each bus line under ideal conditions (Scenario 1) and Table 5 shows the energy comsumption range per line. The energy grows linearly with payload mass. Line 2 exhibits the steepest slope due to its longer distance.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Line ID** & **Distance (km)** \\ \hline Line 1 & 6.78 \\ Line 2 & 14.74 \\ Line 3 & 19.01 \\ Line 4 & 11.23 \\ Line 5 & 13.47 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Total Length of Simulated Bus Lines.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Drone mass (without payload) & 1.8 kg \\ Payload mass range & 1–10 kg \\ Battery capacity & 5870 mAh \\ Average cruise speed & 10 m/s \\ Average flight autonomy & 45 min \\ Max operational radius (1-way) & 8.2 km \\ Powertrain efficiency \(\eta_{PWT}\) & 0.85 \\ Avionics power \(P_{\text{avionics}}\) & 5 W \\ Drag coefficient \(C_{x}\) & 0.03 \\ Air density \(\rho_{\text{air}}\) & 1.225 kg/m\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: UAV Parameters Used in the Simulation.
# Transformer-Based Cascading Reconstruction Network for Video Snapshot Compressive Imaging
## 5 Experiment Results and Analysis
### Time Complexity and Parameter Quantity  
Both time complexity and parameter quantity are important aspects of evaluating video SCI. We run our network, E2E-CNN and RE\({}^{2}\)-Net on GPU and run PnP-FFDNet on MATLAB. The other traditional iterative optimization-based methods are run on CPU. Table 3 compares the average running time required by different methods to reconstruct six simulation datasets and the quantity of parameters of deep network-based methods. The reconstruction time, reconstruction frames per second (FPS) and parameters quantity for different methods are respectively listed in the table. The reconstruction time of GAP-TV, GMM-TP, MMLE-MFA and MMLE-GMM is much longer than that of methods based on a deep network. The proposed network takes 6.7346 s to reconstruct a video dataset, and the FPS is 4.7516. Although the reconstruction time of the proposed network is longer than that of RE2-Net and E2E-CNN, it has a lower parameters quantity than RE\({}^{2}\)-Net and a better reconstruction quality than E2E-CNN. It can be seen that the proposed network achieves a good balance between the complexity of the model and the reconstruction speed while improving the reconstruction quality at a reasonable computational cost.
## 3 Results and Discussion
### Preparation and Characterization of Rubber Composites
#### 3.2.2 Strain Sweep Tests  
Strain sweep experiments were performed, using a rubber process analyzer (RPA), as described in the Materials and Methods section. Storage (\(G^{\prime}\)) and loss (\(G^{\prime\prime}\)) moduli and Tan Delta\({}_{\max}\) (\(G^{\prime\prime}\)/\(G^{\prime}\))  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Composite n.** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline
**Filler Type** & HSAG & HSAG & HSAG & HSAG-SP & HSAG-SP & HSAG-SP \\
**Filler Amount** & 5 phr & 15 phr & 24 phr & 5 phr & 15 phr & 24 phr \\ \hline \(M_{\mathrm{L}}\) **(dNm)** & 0.3 & 0.8 & 1.3 & 0.6 & 0.9 & 1 \\ \(M_{\mathrm{H}}\) **(dNm)** & 6.1 & 8.4 & 10.2 & 6.4 & 8.2 & 9.1 \\ \(M_{\mathrm{H}}-M_{\mathrm{L}}\) **(dNm)** & 5.8 & 7.6 & 8.9 & 5.8 & 7.3 & 8 \\ \(t_{\mathrm{90}}\) **(mm)** & 3.9 & 4.0 & 3.8 & 4.3 & 4.2 & 3.8 \\ \(t_{\mathrm{s1}}\) **(dNm)** & 2.5 & 2.5 & 2.3 & 2.7 & 2.5 & 2.3 \\ \((M_{\mathrm{H}}-M_{\mathrm{L}})\)/\((t_{\mathrm{90}}-t_{\mathrm{s1}})\) & 4.1 & 5.1 & 5.9 & 3.6 & 4.3 & 5.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Curing data of NR–HSAG and NR–HSAG-SP compounds.  
were measured. The obtained results are reported in Table 4. Low levels of moduli were obtained, as expected, in consideration of the low amount of the graphitic filler. It should be also considered that the nanographite is made of graphene nanoplatelets, disposed parallel to the rotors of the RPA. Composites with lamellar nanographite have been reported to have mechanical anisotropy, revealing an orthotropic and transversally isotropic response: modulus values were observed to be very similar in all directions in the sheet plane and much larger (almost double) in the orthogonal direction [66].  
Values of \(\Delta G^{\prime}\) and Tan Delta\({}_{\text{max}}\) increase with the HSAG content, as commonly observed in filled rubbers. The \(\Delta G^{\prime}(G^{\prime}_{0.1\%}-G^{\prime}_{25\%})\) value is taken as an indicator of the so-called Payne Effect, which expresses the non-linearity of the viscoelastic modulus due to the disruption of the filler network formed by the interaction of the filler particles, either directly or mediated by polymer layers [78; 79]. Hence, the behavior at low strain of the nanocomposites, based on either HSAG or HSAG-SP, is similar at the same filler content.
# _Applied sciences_
## 3 Methodology  
### K-Means Clustering Algorithm  
First, the algorithm selects K points from a sample image as the initial clustering centers, estimated as K = 7 in this paper. Second, the distance from each sample to the cluster is calculated, and the samples are classified into the nearest cluster center. Then, new cluster centers are calculated based on the classified samples. Finally, the above steps are repeated until the clustering centers of the two adjacent iterations no longer change. When using the K-means clustering algorithm to segment painted images, it is necessary to transform the images to the Lab color space to enhance the robustness of the algorithm [17]. However, the K-means clustering algorithm still has the two following limitations: (1) The number of initial clustering centers, K, needs to be given to determine the initial division. However, K is usually difficult to estimate. (2) Noise and isolated points significantly impact the segmentation results. This makes it difficult for K-means to achieve color consistency and reach the desired accuracy when performing full-cluster segmentation with complex painted images as input.
## 2 Materials and Methods
### Change in the Form Factor for RF Phase  
When the RF phase difference was +15 degrees, the electron bunch shape could be approximated by a Gaussian distribution. Therefore, to investigate the dependence of the electron bunch shape on the RF phase difference, a ratio of the spectrum at the phase difference \(\Delta\) to the spectrum at the phase difference +15 degrees, O\({}_{\Delta}\)/O\({}_{+15}\), was calculated. Figure 4 shows a graph obtained by dividing the logarithm of this ratio by the angular frequency. It was found that the function ln(O\({}_{\Delta}\)/O\({}_{+15}\))/\(\omega\) could be linearly approximated in a frequency region ranging from 0.4 to 1.6 THz where the CER intensity was sufficiently high and the diffraction loss was negligible. As the ratio O\({}_{\Delta}\)/O\({}_{+15}\) was equal to the ratio of the form factor, this function suggested that the form factor at the RF phase difference \(\Delta\), \(f_{\Delta}(\omega)\), could be expressed with coefficients \(\alpha_{\Delta}\) and \(\beta_{\Delta}\) as the following equation:  
\[f_{\Delta}(\omega)=C_{q}f_{+15}(\omega)\exp\Bigl{(}\alpha_{\Delta}\omega^{2}+ \beta_{\Delta}\omega\Bigr{)} \tag{5}\]  
where \(f_{+15}(\omega)\) is the form factor at \(\Delta=+15^{\circ}\) and can be approximated by Equation (4) in the frequency region higher than 0.4 THz. \(C_{q}\) is a coefficient that depends on the ratio of the electron number in the bunch that contributes to the form factor in the frequency region. The form factor was given by using Equation (4) for Equation (5) with three coefficients (\(a_{\Delta}\), \(b_{\Delta}\), and \(c_{\Delta}\)) as the following equation:  
\[f_{\Delta}(\omega)=\exp\Bigl{(}a_{\Delta}\omega^{2}+b_{\Delta}\omega+c_{\Delta }\Bigr{)} \tag{6}\]  
As the form factor had to be under 1 [24], \(a_{\Delta}\) was negative. Table 1 shows the coefficients \(a_{\Delta}/(2\pi)^{2}\) and \(b_{\Delta}/2\pi\) calculated, which were calculated using the least squares method for the measured spectra shown in Figure 3. It was found that the coefficient of determination was close to 1 in the fitting calculation for any spectrum and the form factor could be expressed with Equation (6). The frequency at which the measured CER spectrum was maximized was highest on-crest operation of the RF field. The frequency, \(f_{M}\), at which
###### Abstract  
This study presents a compact and stable gain spacecraft antenna that operates in all Global Navigation Satellite System (GNSS) bands from 1.164 GHz to 1.610 GHz. The proposed antenna structure based on the single-feed crossed bowtie antenna concept consists of four triangular patches excited with a 90\({}^{\circ}\) phase difference in between to generate right-hand circular polarization (RHCP), without needing complex feed networks. The radiator part of the antenna is covered by a radome and is also supported by a cylindrical dielectric cavity frame (DCF) to weaken the diffracted waves propagating along the ground plane while increasing vibration resistance. The fabricated antenna provides a return loss better than 10 dB with lower than 3 dB axial ratio and a stable gain around 7.2 \(\pm\) 0.3 dBic over the entire GNSS bands, as well as a more compact and lightweight structural performance. It is also verified that the structural integrity and functional performance of the fabricated antenna remain consistent despite exposure to an equivalent vibration level in the launch process. The presented all-band spacecraft GNSS antenna is an innovative implementation with space industry insight for multi-band space applications that have application-specific limitations and provides consistent performance, as well as operational safety with the antenna design simplicity.  
GNSS antenna; stable gain; space application; wideband +
Footnote †: journal: Applied Sciences