esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Spatial Patterns in SMAP/CYGNSS \(\beta\) Values
#### 3.3.4 Landcover Class and \(\beta\) Values  
SMAP/CYGNSS \(\beta\) values also vary with MODIS IGBP landcover class (described in Section 2.3), as shown in Figure 9e. We only included \(\beta\) values in this analysis for 9 km grid cells that did not change landcover class during the period of our linear regression calculations (April 2017 through December 2020) and for 9 km grid cells that were greater than 90% the predominant landcover class by area. Since high topographic roughness produces poorly correlated SMAP/CYGNSS linear regressions and is not dependent on landcover class, we did not include any 9 km grid cells with topographic roughness values greater than 350 m in our landcover class analysis. Areas with topographic roughness greater than 350 m have \(\beta\) values of zero or higher more than 25% of the time (Figure 9d). We did not include landcover classes that are masked out in NSSM products (0: water, 11: permanent wetland, 13: urban and built-up, and 15: snow and ice), nor did we include landcover classes without any 9 km grid cells meeting the criteria explained above (3: deciduous needleleaf forest).  
\(\beta\) values are lowest in croplands (Figure 4a) and grasslands (Figure 4b) and highest in forests (Figure 4e) and barren lands (Figure 4f). Many of the landcover class and \(\beta\) value relationships can be explained by variations in NSSM variability, mean annual precipitation, and mean NDVI throughout landcover classes (Figure 7d-g). We consider how to untangle these confounding effects in the discussion.
# Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management
## 4 Experiment and Simulation
### Simulation of Formation Health Optimization Management  
The performance of the health optimization management algorithm can be assessed via the health loss value when the robot group reaches the optimal configuration. In the implementation of health optimization management algorithms, the goal is to maintain the health of all robots as much as possible and reduce disproportionate health degradation. This article proposes and studies four scenarios:  
1. None of the robots in the group have any abnormal health behaviors;
2. The health degradation rate of a single robot increases;
3. The health value of a single robot at the beginning of the task is lower than other members of the group;
4. The health of a single robot decreases suddenly.  
These scenarios represent some common expected health problems. Scenario 1 represents the best-case scenario, in which no robot suffers from abnormal health behaviors, so the health optimization management algorithm is used to optimize the performance of each robot. Scenario 2 may occur during a period of engine failure, reducing power efficiency. Scenario 3 may indicate that the battery is not fully charged at the beginning of the task. Scenario 4 may be a small component failure, such as a structural fracture. Under these circumstances, the deterioration of the operational status or failure does not require immediate recovery, but continued operation may affect the overall future operational status and performance of the formation.  
In these four scenarios, the four robots form a square formation as an example. During the formation, the health degradations described in the above four scenarios may be encountered. For each scenario, two simulations were performed: one for normal operation and the other for health optimization management.
# A Method to Produce vsiRNAs in Plants with Cross-Kingdom Gene Silencing Capacity
## 2 Materials and Methods
### cDNA Synthesis from sRNAs and Amplification of Krt18 vsiRNA by PCR  
The sRNA cDNA (including the vsiRNAs) were synthesized using the microScript microRNA cDNA Synthesis Kit (Norgen Biotech Corp, Thorold, ON, Canada) following the manufacturer's instructions, starting with 1 \(\upmu\)g of total RNA from plants with 28 days postinoculation (dpi) with pEuMV-YP:Krt18 (Figure 1). When plants defend themselves against viruses through the gene silencing system, they produce vsiRNAs that can cover the entire viral genome [25], so it is expected that vsiRNAs covering the entire 432 bp sequence of Krt18 will be generated. Therefore, to quantify the concentration of generated vsiRNAs, a region of the Krt18 sequence with a Tm of 60 \({}^{\circ}\)C was randomly chosen. Starting with 25 ng of cDNA, the amplification of the Krt18 vsiRNA was performed using the primer  
Figure 1: Scheme of the construction of pEuMV-YP:Krt18 vector and production of virus-derived small interfering RNAs (vsiRNAs) in plants with cross-kingdom gene silencing potential (created in bioreender.com).  
Forward 5\({}^{\prime}\)-CCAGACCGAGAAGGAGACCATG-3\({}^{\prime}\) and Universal PCR Reverse Primer through Maxima SYBR Green qPCR Master Mix (Thermo Scientific, Wilmington, NC, USA). The amplification protocol consisted of 1 cycle at 94 \({}^{\circ}\)C for 3 min and 40 cycles at 94 \({}^{\circ}\)C for 15 s, 60 \({}^{\circ}\)C for 30 s, and 72 \({}^{\circ}\)C for 45 s, using a thermal cycler CFV96 Real-Time System (BioRad Laboratories Inc., Singapore).  
For quantification, the PCR product of the Krt18 vsiRNA cDNA was used as the DNA standard. Dilutions of 1.5 \(\times\) 10\({}^{7}\), 1.5 \(\times\) 10\({}^{6}\), 1.5 \(\times\) 10\({}^{5}\), 1.5 \(\times\) 10\({}^{4}\), and 1.5 \(\times\) 10\({}^{3}\) MM were performed. PCR was performed as described in the previous section. The logarithmic values of vsiRNA concentrations (\(x\)-axis) were plotted against the CT values (\(y\)-axis). From the fitted linear equation, the vsiRNA concentrations were estimated.
###### Abstract  
The tracking of satellite signals with the passive linearly polarized embedded global navigation satellite system (GNSS) antenna of smartphones in dynamic scenarios is susceptible to the changing multipath and obstructions in urban environments, which lead to a significant decrease in the availability and reliability of GNSS solutions. Accordingly, based on the characteristics of smartphone GNSS and inertial measurement unit (IMU) sensors data in GNSS-degraded environments, we established an IMU-aided uncombined precise point positioning (PPP) mathematical model that is suitable for smartphones. To enhance the reliability of initial alignment in dynamic mode, the step function variances depending on carrier-to-noise density ratio were established with the variances of GNSS measurements, and the inertial navigation system (INS) parameters were initialized while both the velocity of smartphones and the position dilution of precision (PDOP) reached corresponding thresholds. Considering the measurement noise and observations gaps of smartphones, the robust Kalman filter (RKF) with equivalent variance matrix was used for parameter estimation to improve the convergence efficiency of the coupled PPP/INS model. Experimental results indicated that the proposed PPP/INS method can effectively improve the positioning performance of smartphones in GNSS-degraded environments. Compared with the conventional smartphone PPP scheme, the PPP/INS horizontal errors in the eastern and western areas of the long trajectory experiment decreased by 49.37% and 48.29%, respectively. Meanwhile, the trajectory deviation of smartphones can remain stable in the tunnel where GNSS signals are blocked.  
precise point positioning: Android smartphone; inertial navigation system; GNSS-degraded environment; robust Kalman filter 2022
# Variational Quantum Circuits for Machine Learning.
## 3 Quantum Approaches for Machine Learning
### Quantum Convolutional Neural Networks  
Convolutional neural networks (CNNs) are a type of neural network in which neurons act as receptive agents, mimicking neurons in the primary visual cortex of a biological brain. The main applications of this type of network are related to visual computer tasks, such as, for example, image classification and segmentation, precision medicine or optical character recognition [51].  
Figure 4: Variational quantum algorithm that applies the function \(f(x,\theta)=z\), with a state preparation circuit \(\mathbb{S}_{X}\), where an input x is encoded, a model circuit \(\mathbb{U}_{\theta}\) where the parameters \(\theta\) are applied, and the measurement stage.  
A CNN generally consists of three layers: a convolution layer, a pooling layer and fully connected layer. The convolution layer computes new pixel values from a linear combination of neighboring pixels with specific weights. The pooling layer aims to reduce the size of the feature map. Finally, the fully connected layer obtains the output through a linear combination of the remaining pixels with specific weights. The weights are calculated through training the neural network with an input dataset.  
In the quantum version of this neural network, there is an additional first step of the convolution layer, encoding the image data into a quantum state.  
The developed model of a quantum convolutional neural network (QCNN) in [52] performs well against noise in image recognition applications. In addition, the parameters it uses are independent of the size of the inputs, making it a suitable solution for currently available noisy quantum devices.  
In study [53], a QCNN is used for quantum phase recognition (QPR) applications. In the experiment, the model discovers whether a quantum state belongs to a particular quantum phase. In addition, quantum error correction (QEC) optimization is performed, which consists of optimizing error correction in real experimental environments when the error model is unknown.  
QCNNs require fewer parameters to obtain similar results compared to conventional convolutional neural networks. Therefore, both quantum and conventional large-scale neural networks can solve complex machine learning problems, but as the growth of Hilbert spaces is exponential, the quantum alternative is more efficient.
# Automatic Spatial Audio Scene Classification in Binaural Recordings of Music +
## 2 Materials and Methods
### Corpus of Binaural Audio Recordings
#### 2.2.2 Database of Binaural Room Impulse Responses
Despite the lack of front-back symmetry in the loudspeaker layout, all the BRIRs from the above set were incorporated in the study. Informal auditioning of the synthesized stimuli, using the above BRIRs, revealed that the excerpts representing the _BF_ scene had distinctively different timbral characteristics (being perceived as \"duller\") and appeared to be elevated, compared to those exhibiting the _FB_ scene. The elevation effect could be explained by the fact of using binaural responses from the widely spaced loudspeakers (\(\pm\)110\({}^{\circ}\)), as demonstrated by [PERSON] [39]. The BRIR set No. 8 (tvtui) also contained the binaural impulse responses acquired using a standard five-channel loudspeaker layout, with the speakers located at 0\({}^{\circ}\), \(\pm\)30\({}^{\circ}\), and \(\pm\)120\({}^{\circ}\), respectively [40]. In order to avoid the above-mentioned timbral coloration and the elevation effect, only the BRIRs from the three frontal loudspeakers were retained in the study (angles of 0\({}^{\circ}\) and \(\pm\)30\({}^{\circ}\)). The binaural impulse responses of the virtual rear loudspeakers were generated using the front-back mirroring technique. In the case of the BRIR set No. 9 (tvtui2) [41], the measurements were undertaken in the same venue (TV studio) as in the case of the previous set. However, in contrast to the previous BRIR set, the distance between the dummy head and the loudspeakers was reduced from 3.5 to 2 m. Moreover, a different loudspeaker type was utilized. In the case of the BRIR set No. 9 the measurements from 10 loudspeakers were provided, with 5 loudspeakers placed at the front (at the directions of 0\({}^{\circ}\), \(\pm\)15\({}^{\circ}\), \(\pm\)30\({}^{\circ}\), respectively) and the remaining 5 loudspeakers positioned symmetrically at the back. All the measurements were incorporated into the study. In the case of the BRIR sets Nos. 10 and 11 (labtui and rehabtui), the same measurement layout was used as in the previous set (10 loudspeakers). The BRIRs contained in these two sets were captured in the listening room and the rehabilitation laboratory, respectively [41]. The last two sets of BRIRs (Nos. 12 and 13), signified as tuburo250 and tuburo310, contained the measurements from a 64-channel loudspeaker array of rectangular shape, intended to use for wave-field synthesis. For both sets the measurements were undertaken in the same laboratory, but with different levels of acoustical dumping, resulting in two different reverberation times equal to 250 and 310 ms, respectively [42].
# A Comparative Analysis of IMUs and Optical Systems in Layup Shot Biomechanics
## 5 Implications  
The findings of our study, alongside the existing literature, highlight the utility of wearable IMUs in providing accessible, portable, and cost-effective solutions for field-based sports biomechanics. IMUs like Xsens are particularly effective for assessing global metrics and sagittal plane kinematics, making them suitable for applications in performance monitoring, skill acquisition analysis, and injury prevention strategies. However, the study's findings emphasise the need for caution when using IMUs for detailed kinematic analyses, particularly for tasks involving high velocities or complex multi-planar dynamics.  
Xsens demonstrated strong capability in capturing and detecting relative biomechanical changes under varying conditions, highlighting its practicality for applied sports biomechanics, particularly in real-world, non-laboratory settings where optical systems like Vicon may not be feasible. Its ability to identify kinematic adaptations, such as those athletes make in response to external constraints like defensive pressure, reinforces its value as a robust alternative for performance analysis. The comparable results between Xsens and Vicon in differentiating defended and undefended conditions further emphasise its reliability in applied biomechanics research.
# The Application of Computed Tomography to Study the Soil Porosity of Mountain Red Earth
## 1 Introduction
Initially, the technology was widely used in the fields of petroleum, geology, and mining and later introduced into geological structure and soil research, providing a new perspective for quantitative structural characterization [19; 20; 21; 22; 23]. For example, through 3D reconstruction, CT technology enables researchers to visually compare water migration paths under different land use types and observe the long-term effects of cultivation on soil physical properties [24; 25; 26; 27; 28]. In the study of peatland, CT technology has achieved the first extraction of the peat deposit porous network from umCT images, filling the gap in the study of peatland porosity characteristics [29]. CT technology has offered significant insights into the assessment of the physical properties and compressibility of rehabilitated soils [30]. Additionally, it has been utilized to investigate the impact of long-term application of cow manure and inorganic fertilizers on the porosity structure of soils in corn-soybean rotation systems [31]. Its non-invasive nature and capability for precise quantitative analysis have provided a robust foundation for further research in the fields of soil science and geology [32]. Mountain Red Earth is a unique type of soil widely distributed across subtropical and tropical regions. Despite its ecological importance, there is still a limited understanding of how it interacts with different agricultural practices, particularly the use of organic and inorganic amendments. This knowledge gap may affect the sustainable management of these soils, especially in areas prone to erosion. To address this issue, our study proposes using computed tomography (CT) technology to scan soil columns from Mountain Red Earth. Previous research shows that this soil has not been thoroughly studied using non-destructive methods to examine its pore structure, especially in eroded areas. By applying CT scanning, we aim to provide a clear and detailed view of the pore structure in MountainRed Earth. This approach could help improve our understanding of how soil erosion occurs and suggest better ways to manage the soil, leading to more sustainable agricultural practices and better soil conservation.
## 4 Performance Analysis
### Average Probability of Successful Transmission  
Proposition 2 in [11] analyzed the average successful transmission probability of a packet, but only considers the successful transmission of the first transmitted packets, which is not accurate enough. However, the packets received successfully in each slot are divided into two parts in this paper: newly arrived packets and retransmission packets. Thus, we give a more reasonable expression. When the system is in a steady state, the average successful transmission probability is the ratio of throughput to the average number of transmitted packets, denoted by \(P\). Therefore, \(P\) can be expressed by the following equation:  
\[P=\frac{T}{p_{f}\cdot(M-R)+p_{r}\cdot R}. \tag{20}\]
## 3 Kriging-Based Probability Density Estimation for FCTP
### Initial Surrogate Model of FCTP  
For initial training samples, various sampling methods are available, such as random sampling (RS), Latin hypercube sampling (LHS), and uniform sampling (US). At high dimensions, sample collection in significant space cannot be guaranteed using RS. LHS and US ensure that sampling points are as dispersed as possible. However, the stochastic properties of LHS method may lead to the instability of the surrogate model when the training samples are few. Therefore, in this paper, the US method is used to generate initial training samples. In addition, the transformation of all random variables involved in the reliability problem from the space to which they belong to standard normal space through Nataf transform [36] facilitates the computation. A larger sampling range is not only slow to converge, but fitting accuracy is also reduced, especially for nonlinear problems. Based on the \(3\sigma\) principle, the initial sample range in standard normal space is determined as \([-3,3]\), and the range is continuously reduced during the iteration process. According to Shi [37], the number of initial sampling points is generally 10-15 to ensure the accuracy of the model and the efficiency of active learning. Also, considering the effect of dimensionality determines the number of initial samples to be  
\[N_{0}=max(2d+1.12) \tag{18}\]  
where \(d\) is the number of dimensions. The initial samples can be represented as follows:  
\[\mathbf{Z}^{initial}=\begin{bmatrix}z_{1}^{(1)}&z_{2}^{(1)}&\cdots&z_{d}^{(1)}\\ z_{1}^{(2)}&z_{2}^{(2)}&\cdots&z_{d}^{(2)}\\ \vdots&\vdots&\ddots&\vdots\\ z_{1}^{N_{0}}&z_{2}^{N_{0}}&\cdots&z_{d}^{N_{0}}\end{bmatrix} \tag{19}\]It can also be expressed as:  
\[\mathbf{Z}^{initial}=\left[\mathbf{z}_{1}{}^{initial},\mathbf{z}_{2}{}^{initial},\ldots,\mathbf{z}_{ N_{0}}{}^{initial}\right]^{T} \tag{20}\]  
where \(\mathbf{z}_{i}{}^{initial}=\left[\mathbf{z}_{i}^{1},\mathbf{z}_{i}^{2},\ldots\mathbf{z}_{i}^{d} \right],i=1,2,\ldots,N_{0}\).  
After generating the initial training sample \(\mathbf{z}_{i}{}^{initial}\), the performance function \(g(\mathbf{Z},t)\) can be viewed as univariate function \(g\left(\mathbf{z}_{i}{}^{initial},t\right)\) of time. Thus, the FCTP \(ct_{i}{}^{initial}\) of initial training sample can be solved by the following equation:  
\[g\left(\mathbf{z}_{i}{}^{initial},t\right)=0 \tag{21}\]  
The initial set of training samples \(\left\{\mathbf{z}_{i}{}^{initial},ct_{i}^{initial}\right\}\), \(i=1,2,\ldots,N_{0}\) is obtained. Further, the initial Kriging surrogate model of FCTP is constructed using Equation (8). The subsequent \(j\)th iteration is denoted \(\hat{\mathbf{\vartheta}}^{(j)}(\mathbf{Z})\).
# Enabling Intelligent 6G Communications: A Scalable Deep Learning Framework for MIMO Detection
## 5 MIMONet: A DL-Based MIMO Detector
### MIMONet Network Architecture  
The MIMONet network architecture consists of several key components: an input layer, three hidden layers, and an output layer. The feature data, which includes both real and imaginary components of the received signal and channel matrix, is passed through the network to output the predicted transmitted symbols. The structure of the MIMONet architecture allows the model to learn intricate relationships between the input features and the output symbols, optimizing the detection process. This process is further broken down based on network layers:  
* Input Layer: In the input layer, the received signal \(\mathbf{r}\) and channel matrix \(\mathbf{G}\) are processed by first separating their real and imaginary parts: \[\mathbf{r}=\mathrm{Re}(\mathbf{r})+j\,\mathrm{Im}(\mathbf{r}),\quad\mathbf{G} =\mathrm{Re}(\mathbf{G})+j\,\mathrm{Im}(\mathbf{G})\] (21) The real and imaginary components of the received signal and channel matrix are concatenated into a feature vector \(\mathbf{X}\) that passes through the network's hidden layers, which process and learn the patterns present in the data.  
Figure 1: Systematic architecture of the MIMONet detector.  
* Hidden Layers: Each hidden layer of the MIMONet consists of 128 neurons and employs a fully connected layer along with a clipped ReLU activation function to introduce nonlinearity. The output of the first hidden layer \(\mathbf{h}_{1}\) is computed as follows: \[\mathbf{h}_{1}=\phi_{1}(\Psi_{1}\cdot\mathbf{X}+\beta_{1})\] (22) where \(\Psi_{1}\) and \(\beta_{1}\) represent the weight matrix and bias vector of the first hidden layer, and \(\phi_{1}\) is the activation function (clipped ReLU). Similarly, the second and third hidden layers produce the outputs \(\mathbf{h}_{2}\) and \(\mathbf{h}_{3}\) as \[\mathbf{h}_{2}=\phi_{2}(\Psi_{2}\cdot\mathbf{h}_{1}+\beta_{2})\] (23) \[\mathbf{h}_{3}=\phi_{3}(\Psi_{3}\cdot\mathbf{h}_{2}+\beta_{3})\] (24) where \(\Psi_{2},\Psi_{3}\) are the weight matrices for the second and third hidden layers, \(\beta_{2},\beta_{3}\) are the corresponding bias vectors, and \(\phi_{2},\phi_{3}\) are the activation functions for the second and third hidden layers. These hidden layers allow the network to progressively learn more abstract features of the received signal and channel data.
* Output Layer: The final output layer of the network produces the predicted transmitted symbols \(\mathbf{\hat{s}}\). This output is obtained by applying an argmax function to the output of the third hidden layer: \[\mathbf{\hat{s}}=\text{argmax}(\Psi_{4}\cdot\mathbf{h}_{3}+\beta_{4})\] (25) Similarly, \(\Psi_{4}\) and \(\beta_{4}\) represent the weight matrix and bias vector for the output layer, respectively. This operation identifies the most likely transmitted symbol, as predicted by the MIMONet detector.