esa-sceva/satcom-chunk-collection huggingface dataset:
# Structural Optimization of MicroMED Dust Analyzer
## 3 Results
### Optimal OB--Model A  
The rough optimal solution to be post-processed was obtained through three iterative TO cycles, whose outcomes are presented in Figure 8. The overall computational time required for completing the optimization was about 48 h, using an Intel(r)Core(tm) i7-12700H processor. On the other hand, the definitive Model A design is depicted in Figure 9. Assigning Al7075-T6 as material (i.e., having a density of about 2810 kg/m\({}^{3}\)) a mass of 64.63 g was estimated, of which 3.18 g were allocated to the ribs grid.  
For the Model A, the FEM comprised 32,042 finite elements, split between 28,282 solid tetrahedrons and 3767 shell triangles. The FEAs results are summarized in Table 2, while the most critical loading conditions (i.e., 1000 m/s\({}^{2}\) acceleration along the \(Y\)-axis for the quasi-static analysis and the cold case for thermoelastic stresses) are reported in Figure 10. In particular, a first natural frequency of about 412 Hz was estimated, while a maximum Von Mises stress of 107 MPa (i.e., leading to a _MoS_ of 2.12) and 11 MPa (i.e., leading to a _MoS_ of 29) was found for the quasi-static and the thermoelastic analyses, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Resonances [Hz]**} & \multicolumn{4}{c}{**Von Mises Quasi-Static Stresses [MPa]**} \\ \hline f\({}_{1}\) & f\({}_{2}\) & f\({}_{3}\) & f\({}_{4}\) & f\({}_{5}\) & \(\sigma\chi\) & _MoS_ & \(\sigma\gamma\) & _MoS_ & \(\sigma\)\(Z\) & _MoS_ \\
412 & 521 & 627 & 745 & 791 & 39 & 7.50 & 107 & 2.12 & 50 & 5.74 \\ \hline \hline \multicolumn{10}{c}{**Von Mises Thermoelastic Stresses [MPa]**} \\ \hline  & Cold case & & _MoS_ & & Hot case & & & _MoS_ & \\  & 11 & & 29 & & 4 & & 88 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: MicroMED Model A OB FEAs outcomes.  
Figure 8: TO iterative cycles results (blue colour) for the Model A.  
Figure 9: Model A without (**a**) and with (**b**) the stiffening ribs grid. FEAs features (i.e., lumped masses, rigid links, shells, and constraints) are highlighted. The mesh was applied to the model (**c**).  
The thermal deformations are reported in Figure 11. Negligible relative displacements were obtained (i.e., 581 nm and 499 nm for the cold case and hot case, respectively), validating the thermo-mechanical behaviour of the proposed solution. The displacements measured at the probes in the three directions with respect to the reference frame XYZ are reported in Appendix B.
## 5 Conclusions  
In this study, TVDI was utilized to evaluate the spatiotemporal patterns of drought in 2022, and three remote sensing vegetation indices (NDVI, LAI, and GPP) were used to explore the vegetation response to drought stress in the karst areas of Guilin city. Drought mainly occurred from July to December in 2022. Notably, the more severely drought-affected areas were concentrated in the karst areas, where both the severity and duration of drought were significantly greater than those in non- karst areas. The different vegetation indices displayed distinct response patterns to drought stress. We found that using TVDI alongside NDVI, LAI, and GPP enables us to accurately capture the spatiotemporal distribution of drought and its impact on vegetation ecosystems, particularly regarding their correlation with the drought intensity and duration in karst areas. Further, it appears well suited for detecting the intensified impacts of drought on regional vegetation ecosystems in the unique topographical conditions of karst areas. Our findings also enhance the theoretical understanding of drought coping mechanisms, supporting rapid drought assessment and ecological vegetation restoration. We recommend that Guilin city enhance the diversity of planted vegetation to mitigate the impacts of drought on the ecosystem.  
Conceptualization, Y.Y. and W.H.; data curation, Z.G. and J.H.; funding acquisition, Y.Y. and W.H.; investigation, Z.G. and W.H.; methodology, W.H. and Z.G.; writing--original draft, Z.G.; writing--review and editing, W.H. and Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Research and Development Program of Guangxi (No. GuikeAB22035060), the National Natural Science Foundation of China (No. 32060369), the National Natural Science Foundation of Guangxi (No. 2024 GXNSFAA010318), the Basic Research Fund of Guangxi Institute of Botany (No. GUI ZHYE 23005), and the Fund of the Guangxi Key Laboratory of Plant Conservation and Restoration Ecology in Karst Terrain (No. 22-035-26).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
# Research Advances and Application Prospect of Low-Temperature Plasma in Tumor Immunotherapy
## 4 Activation of Anti-Tumor Immunity by LTP
### The Impact of LTP on Non-Specific Immune Responses
#### 4.1.3 LTP Effects Other Non-Specific Immune Cells  
Neutrophils are one of the immune cells existing in the tumor microenvironment. Tumor-associated neutrophils have been proven to have functional plasticity, whose functions and phenotypes are polarized differently at different stages of the tumor [35]. The formation of neutrophil extracellular traps (NETs) increased after LTP treatment [36]. [PERSON] et al. [37] used LTP-activated water to gavage immunodeficiency mice, and found that neutrophils and monocytes in the blood slightly increased. [PERSON] et al. [32] injected PAM into mice seven days after tumor challenge and analyzed the changes in intratumoral immunological profile. The results demonstrated the number of neutrophils increased in the treatment group. However, there are no reports on the effects of LTP on other innate immune cells such as NK cells and mast cells. Our team has explored the effects of plasma-activated saline on NK cells and found that an appropriate dose of LTP-activated saline promoted the vitality and killing activity of NK-92 MI cells, while excessive doses inhibited their viability and killing activity.
## 4 Experiment and Analysis  
### Datasets  
In our work, two datasets are used: the MICCAI 2013 SATA Challenge (MICCAI) dataset and the Harmonized Protocol initiative of the Alzheimer's Disease Neuroimaging Initiative (HarP) [42]. The MICCAI contains 35 groups of T1-weighted images in the training set and 12 groups in the testing set; every training image has its own corresponding multi-atlas label. Every image in this dataset is in NIFTI format, and both images and labels are 256 \(\times\) 256 \(\times\) 287 pixels, with a voxel spacing of \(1\times 1\times 1\) pixels. The MICCAI dataset can be accessed publicly at [[https://my.vanderbilt.edu/masi/workshops/](https://my.vanderbilt.edu/masi/workshops/)]([https://my.vanderbilt.edu/masi/workshops/](https://my.vanderbilt.edu/masi/workshops/)) (accessed on 15 April 2023). The HarP contains 135 groups of T1-weighted MRI images and their corresponding hippocampus labels. All of the images and labels have a voxel size of \(1\times 1\times 1\) pixels and a resolution of 197 \(\times\) 233 \(\times\) 189 pixels. The HarP dataset can be accessed publicly at [[http://www.hippocampal-protocol.net](http://www.hippocampal-protocol.net)]([http://www.hippocampal-protocol.net](http://www.hippocampal-protocol.net)) (accessed on 23 March 2023).  
For convenience of display, the segmentation labels are mapped in the original images with the same resolution between the raw MRI image and its hippocampus segmentation label in the HarP and the MICCAI datasets. We set the corresponding hippocampus label pixel in the original MRI image to a specific value to represent the hippocampus, and other non-hippocampus pixels were kept unchanged to distinguish them from the hippocampus. The visualization segmentation results of the HarP and MICCAI datasets are displayed in Figures 6 and 7, respectively. The area with a red pixel represents where the hippocampus is located, and other pixel values are non-hippocampus areas.
## 3 Results
### Response of Light Response Curve Residuals to VPD and SVWC at Different Climates  
The applied LRC model reflects only the light response of GPP and thus, its residuals can be used to assess the influence of additional meteorological parameters (i.e., VPD and SVWC) that are known to affect GPP (Figures 3 and 4). The residual analysis confirmed that the residuals consistently tended to be more negative with increasing VPD and decreasing SVWC (dry conditions). VPD had a significant and stronger effect on GPP than SVWC across both sites, as seen from the piecewise regression analysis, using the residuals from the LRC for the years under study. However, during the DY period, both VPD and SVWC had significant effects on GPP at CZ-RAJ. For CZ-BK1, there was a minimal effect of SVWC on GPP during the years with normal conditions.  
Generally, the relationship between the residuals and changes in VPD and SVWC revealed a biphasic response to drought, except for the DY period in CZ-RAJ in Figure 3. All the breakpoints from the piecewise regression were found to be statistically significant (Table 7). However, steeper slopes after the VPD breakpoint values from the initial slope were mostly observed for both DY and NY periods in CZ-RAJ and only for DY period in CZ-BK1. This shows that during all the years under study, GPP decreased at a faster rate after the breakpoint in CZ-RAJ than in CZ-BK1, except for the DY period when there was a significant decline in GPP at a faster rate after the breakpoint across both sites.
## 2 Materials and Methods
The kinematics viscosity is represented as \(\upsilon=\frac{\mu_{0}}{\rho}\), \(\Gamma>0\) is the time constant, \(u\) and \(v\) are the velocities along the \(x\)-axis and \(y\)-axis, respectively, \(T\) and \(C\) represent the temperature and concentration fields, respectively, \(\rho\) indicates the density of the fluid, \(C_{p}\) designates the specific heat, \(C_{s}\) represents the absorption susceptibility, liquid film thickness is denoted by \(h\left(t\right)\), \(q_{r}=-\frac{16\sigma T_{0}^{3}}{3k}\frac{\partial T}{\partial y}\) indicates the radiative heat fluctuation, the Stefan-Boltzmann constant is specified by \(\sigma\), the species concentration molecular diffusivity is represented by \(D_{m}\), \(T_{m}\) represents the mean temperature, the thermal diffusion ratio is denoted by \(k_{T}\), and \(k\) designates the thermal conductivity of the liquid film. We introduced the following transformations for the velocity, temperature, and concentration fields:  
\[ \begin{array}{l}\psi(x,y,t)=x\sqrt{\frac{\upsilon b}{1-\alpha t}}f\left( \eta\right),u=\frac{\partial\psi}{\partial y}=\frac{bx}{(1-\alpha t)}f^{\prime }\left(\eta\right)=\frac{\beta^{2}\gamma\gamma}{h^{2}}f^{\prime}\left(\eta \right),\\ v=-\frac{\partial\psi}{\partial x}=-\sqrt{\frac{\upsilon b}{1-\alpha t}}f \left(\xi\right)=-\frac{\gamma\rho}{h}f\left(\eta\right),\eta=\sqrt{\frac{b}{ \upsilon(1-\alpha t)}}y=\frac{\beta}{h}y,\\ T(x,y,t)=T_{0}-T_{ref}\left[\frac{\upsilon^{2}}{2\upsilon}\right]\left(1-at \right)^{-\frac{3}{2}}\theta\left(\eta\right),C(x,y,t)=C_{0}-C_{ref}\left[ \frac{\upsilon^{2}}{2\upsilon}\right]\left(1-at\right)^{-\frac{3}{2}}\varphi \left(\eta\right),\end{array}  \tag{7}\]  
where a prime number specifies the derivative with respect to \(\eta\) and \(\psi\left(x,y,t\right)\) is the stream function; \(\beta=h(t)\sqrt{\frac{b}{\upsilon(1-\alpha t)}}\) is the non-dimensional thickness of the nano liquid film and \(h(t)\) is the uniform thickness of the fluid film, which gives \(\frac{dh}{dt}=-\frac{\beta a}{2}\left[\frac{\upsilon}{b}\right]^{\frac{1}{2}} \left(1-\alpha t\right)^{-\frac{1}{2}}\).
###### Abstract  
The purpose of the current research is to study the microstructure and mechanical properties of Ni-Ti-Ag shape memory alloys prepared by the casting route. Ag (grain size at 1 mm) was incorporated into Ni-Ti alloys at varying percentages of weight (0, 1.5, 3 and 4.5 wt.% Ag) to produce shape memory alloys using a Vacuum Arc Re-melting (VAR) furnace. Microstructural analysis was defined by FESEM microscopy and XRD examinations, while the transformation temperatures of the Ni-Ti-Ag shape memory alloy were determined by DSC examination. On the other hand, determination of mechanical properties was carried out using micro-hardness and compressive tests. The results of this work show that Ag was dispersed homogeneously into the Ni-Ti alloy. Moreover, two primary phases (austenite phase and martensite phase) emerged with few impurities. The results of the XRD examination show that the number of Ag peaks increased with the increase in weight percentage of Ag. The transformation temperature of the austenitic phase was defined as \(-1.6\) \({}^{\circ}\)C by DSC. The mechanical characterizations increased with the increase in weight percentages of Ag (1.5, 3 and 4.5 wt.%), and significantly affected the mechanical properties of the Ni-Ti alloy. An improvement in compressive strength (42.478%) was found for the alloy with 3 wt.% Ag, while the micro-hardness results show a slight decrease in micro-hardness (8.858%) for the alloy with 4.5 wt.% Ag.  
shape memory alloys; Ni-Ti-Ag; casting; VAR furnace; microstructure; XRD; mechanical properties +
Footnote †: journal: Applied Sciences
# Performance Evaluation and Noise Mitigation of the FY-3E Microwave Humidity Sounder
## 5 Observation Noises
The biases of each channel after noise mitigation are shown in Figure 13. Originally, the biases for all the channels have high-frequency oscillations along the scanlines, with basically the same oscillation directions. After the brightness temperature reconstruction, the overall trend of the biases with FOVs does not change, but the high-frequency oscillations are eliminated to some extent, indicating that the along-scan noises are suppressed well. Figure 11: Spatial distribution of the: (**a**) striping noise (unit: K); and (**b**) “O\(-\)B” (unit: K) after the striping noise mitigation for MWHS-2 Channel 3 on 2 October 2021. Figure 12: The along-scanline noises (unit: K) of 600 scanlines for Channel 3. Figure 14 shows the \"O\(-\)B\" standard deviations after noise filtering. Different from the bias changes, the standard deviations are only slightly smoothed in individual channels, with values almost remaining unchanged, further proving that the noise filtering method has little impact on the other signals in addition to the along-scan noise; thus, the error characteristic basically survives.
## 3 Results  
### Parameter Introduction and Experimental Data  
The datasets used in this study consist of GF-3 and Sentinel-1 data. GF-3 datasets used in this study covered the Taihu region and Shanghai city. It was acquired using the Fine Strip II (FSII) imaging mode, with a nominal resolution of 10 m. The Taihu region is a plain urban area with an average elevation of about 4 m, and the data parameters are shown in Table 1. Sentinel-1 data used in this study were acquired in IW mode and were obtained from [[https://datspace.copernicus.eu/data-collections/sentinel-data/sentinel-1](https://datspace.copernicus.eu/data-collections/sentinel-data/sentinel-1)]([https://datspace.copernicus.eu/data-collections/sentinel-data/sentinel-1](https://datspace.copernicus.eu/data-collections/sentinel-data/sentinel-1)) (accessed on 1 July 2025). Due to the large differences in the x and y directions of the raw data, which were 2.7 \(\times\) 22 m, multi-view processing was performed using ENVI 5.6 to obtain 20 \(\times\) 20 m data with roughly the same resolution. The Sentinel-1 datasets cover Hunan Province, China, which has an average elevation of 1000 m and features predominantly mountainous and hilly terrain. The image distribution is shown in Figure 6.  
In the experiments, the response function threshold in SUSAN is mmax/2. The ratio of Gaussian blurs from inner to outer layers in the construction of multiscale feature descriptors is the ratio = \(\sqrt[3]{2}\), and the quantity of layers in the scale space is eight.
# Communication Rate Increase in Drill Strings of Oil and Gas Wells Using Multiple Actuators
## 2 The Experimental Testbed  
The drill string testbed shown in Figure 1a was composed of two steel pipes connected using a coupling. The length and diameter of each pipe were about 1.5 m and 10 cm, respectively, whereas the length of the coupling was about 9 cm. The two transmitters were piezoelectric transducers, shown in Figure 1a. The receivers are shown in Figure 1b and included one strain sensor (PCB model 740B02), and one triaxial accelerometer (PCB model 356B21). These two sensors are needed to separate and demodulate the two data streams sent out simultaneously by the two transmitters, in order to double the transmission rate. The strain sensor measured fractional particle displacement along the drill pipe's \(x\) axis [4], whereas the triaxial accelerometer measured particle accelerations along \(x\), \(y\), and \(z\) axes [4] The strain sensor was used because of the smaller delay spread and therefore led to a less-frequency-selective behavior of strain channels [5], helping to improve data detection and reduce the bit error rate. The triaxial accelerometer was used to explore the performance of the three orthogonal acceleration channels for data detection. The frequency and impulse responses of all these channels were measured and studied, and are discussed in the next section. A schematic drawing of the entire testbed is provided in Figure 1c.  
Figure 1: The drill string testbed: (**a**) two piezoelectric transmitters labeled as Tx 1 and Tx 2; (**b**) two receiver sensors, including one strain sensor labeled as Strain, and one triaxial accelerometer labeled as Triaxial; (**c**) schematic drawing of the testbed.
## 4 Spectrum Sensing in Maritime Cognitive Radio Networks
### Entropy-Based Spectrum Sensing  
Entropy is dependent on the signal power and is highly susceptible to noise uncertainty in the time domain. Therefore, the entropy is calculated in the frequency domain. For this reason, discrete Fourier transform (DFT) is applied on the received signal, \(r(t)\), and we obtain:  
\[\overline{R(k)}=\left\{\begin{array}{ll}\overline{W(k)}&\text{in case of }H_{0}\\ \overline{X(k)}+\overline{W(k)}&\text{in case of }H_{1}\end{array}\right. \tag{8}\]  
where \(k=0,1,...K\) and \(K\) is the size of the DFT. \(\overline{R(k)}\), \(\overline{X(k)}\) and \(\overline{W(k)}\) denote the complex spectrum of \(r(t)\), \(x(t)\) and \(w(t)\), respectively. The spectrum magnitude, \(\left|\overline{R(k)}\right|\), of the measured signal can be represented by the random variable, \(Y\), for which estimation of the probability density function (PDF) is required. Therefore, the detection strategy of the entropy-based frequency model can be expressed as:  
\[H_{L0}(Y)\;vs.\;H_{L1}(Y) \tag{9}\]where \(H_{L0}(Y)\) and \(H_{L1}(Y)\) denote the entropy with the number of states, \(L\), in hypotheses \(H_{0}\) and \(H_{1}\), respectively.  
For simplicity, the histogram method is used to estimate the probability of each state. The number of states of a random variable, \(Y\), is equal to the number of bins, \(L\). Let \(k_{l}\) be the total number of occurrences of the \(lth\) bin; then \(N=\sum\limits_{l=1}^{L}k_{l}\), where \(N\) is the number of samples. The frequency of occurrences of the \(lth\) bin is represented as probability \(p_{l}\), _i.e._, \(p_{l}=k_{l}/N\). The bin width \(\Delta=Y_{m}/L\), where \(Y_{m}\) denotes the maximum spectrum amplitude of the signal [17].  
Then, the entropy can be written as:  
\[E(Y)=-\sum\limits_{l=1}^{L}p_{l}\;logp_{l}. \tag{10}\]  
After the entropy is calculated for the received signal, we employ the threshold decision rule:  
\[D=\left\{\begin{array}{ll}E(Y)>\lambda_{E}&\mbox{in case of $H_{0}$},\\ E(Y)\leq\lambda_{E}&\mbox{in case of $H_{1}$}\end{array}\right. \tag{11}\]  
where \(\lambda_{E}\) is the threshold. When the calculated entropy exceeds the threshold value, it implies the absence of PU and _vice versa_.
# Assessment of Student Music Performances Using Deep Neural Networks
## 2 Related Work
In spite of the different feature sets used by the above works, the central idea that ties them together is that they all attempt to leverage the knowledge of experts to design features tuned for a specific task. While this approach generally works well for simpler tasks, there is a risk of sub-optimalperformance in problems involving complex relationships between the variables. DNNs, on the other hand, are capable of learning features from data and show promise in modeling these complex relationships. Therefore, neural networks and other feature learning methods have rapidly gained traction within the MIR community [36]. Amongst the different classes of deep neural architectures, convolutional neural networks (CNNs) are probably the most widely used. CNNs are inspired by biological vision systems and are efficient at learning local feature representations while possessing useful properties like local invariance and robustness to translation and distortions [37]. Following their success in image classification tasks (compare, e.g., [38]), CNNs have now been used used in numerous audio and music based tasks [28; 39; 40; 41; 42]. Although CNNs are capable of capturing local features, they lack the ability to learn temporal dependencies, which is critical to modeling sequential data such as speech, audio or music. Recurrent neural networks (RNNs) are another class of neural networks designed for sequential data [43] and have been used successfully for MIR tasks such as source separation [10], music transcription [44] and music classification [25]. Although DNNs and other feature learning methods are popular in the field of MIR in general, their usage in the analysis of music performance has been rather limited [23; 45]. A few recent works have shown the potential of unsupervised feature learning using techniques such as sparse coding [46; 47] and sparse filtering [48] in MPA tasks. [PERSON] and [PERSON] used MFCCs and features learned from sparse filtering to detect common mistakes made by beginner flute players [45]. [PERSON] and [PERSON] used sparse coding to learn features for building regression models capable of assessing percussive music performances [23]. The models showed significant improvement in performance compared to those using hand-crafted features. Inspired by the success of these unsupervised feature learning techniques, the motivation of this study is to explore the viability of using supervised feature learning in the form of DNNs for the assessment of music performance.