esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
Table 2 reports the results of the field test reflecting the mean values of maximum air velocity \(v_{x}\) out of the vineyard row and the relative standard deviation (S.D.).  
To compare the maximum velocities (centerline) of the air jet within the canopy that was predicted by combination (10) (----) with the one that was predicted by Equation (6) (----), Figure 4 shows the respective curves of the air velocity decay, \(v_{x}\), vs. the horizontal distance, \(x\), from the outlet of the sprayer. Equation (6) gives incorrect negative values for high \(x\) values. In addition, the experimental \(v_{x}\) value and the relative standard deviation bars are shown in correspondence with the foliage exit \(x_{e}\). There was no significant difference between the experimental value and the predicted value by combination (10).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Geometrical Data** & **Value** \\ \hline \(r_{o}\) (m) & 0.47 \\ \(b_{o}\)(m) & 0.075 \\ \(x_{o}\) (m) & \(-\)0.085 \\ \(x_{t}\) (m) & 0.373 \\ \(x_{t}\) (m) & 0.71 \\ \(x_{e}\) (m) & 1.94 \\ \(v_{x_{i}}\) (m/s) & 7.70 \\ \(v_{x_{t}}\) (m/s) experim. & 0.98 \\ S.D. of \(v_{x_{t}}\) & 0.25 \\ \(\rho_{l}\) (s\({}^{-1}\)) & 11.1 \\ \(v_{x_{t}}\) (m/s) Equation (10) & 1.05 \\ \(v_{x_{t}}\) (m/s) Equation (11) & 0.96 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Geometrical data measured on the crops and on the sprayer as well as measured and predicted air velocities.  
Figure 4: The maximum velocity (centerline) of the air jet in the foliage, predicted by combination (10) (----), is shown in comparison to the predicted maximum velocity by Equation (6) (----), which can result in incorrect negative values and in the experimental value (\(*\)).  
Due to the complexity of Equation (7), which was carried over to the second and third linear piecewise portions, (7\({}^{\prime}\)) and (7\({}^{\prime\prime}\)), respectively, a simpler combination was made based only on Equations (6) and (8). Equation (6) was extended from 7.70 to 2 m/s, and (8) was extended from 0 to 2 m/s, as shown in Table 3.  
The new simplified combination is:  
\[v_{x}=\sum_{n=1}^{2}\Pi_{n}v_{xn}=\Pi_{1}v_{x1}+\Pi_{2}v_{x2}, \tag{11}\]  
where: \(v_{x1}\) is provided by Equation (6) and \(v_{x2}\) is provided by Equation (8).  
The new curve (11) of air velocity decay \(v_{x}\) vs. the horizontal distance \(x\) is shown in Figure 5 with curve (6) and the experimental \(v_{x}\) value. There was no significant difference between the experimental value and the one that was predicted by combination (11). The mean relative error (MRE) of Equation (11) vs. Equation (10) was 3.6%, and the maximum error was 10.7% at \(x=1.63\) m.
# Research on Spaceborne Target Detection Based on Yolov5 and Image Compression
###### Abstract
Based on the horizontal anchor frame, the rotating anchor frame is obtained by full connection learning in the candidate frame stage. Unlike RRPN and other methods that set a large number of rotating anchor frames, the RoI transformer greatly reduces the total number of anchor frames, thus reducing the complexity. Aiming to solve problems such as small targets, unclear identification masks, and fuzzy input images in remote sensing images, we propose a modified Yolov5 network for small target detection. Yolov5 is an excellent neural network in face recognition scenarios with different rotations and sizes. After modifications are made to the Yolov5 network, it can be used to detect small targets with different rotations on a satellite image. Based on Yolov5 and JPEG2000, an on-board target detection and compression transmission system is designed by combining remote sensing target detection technology with compression storage transmission technology. According to the target set, the system can recognize the image data on the satellite, compress and store the regional images in the target set, and feed back to the ground through the telemetry link, effectively improving the transmission efficiency of high-value remote sensing images between the satellite and the ground.
## 3 Analysis of Temporal and Spatial Distribution Characteristics of Landslide Surge
### Characterization of Surge Waveforms
Testing points h07 and h08 are in the same propagation direction with the same number of wave peaks in 20 s. The waveform is relatively close, but the relative wave height will be changed a lot since h08 is not in the central axis of the wave propagation path. The number of wave peaks at testing point h09 change considerably compared with h07 in 20 s; this is due to the fact that testing point h07 and h09 are not in the same propagation direction. Thus, the waveforms appeared to be a big difference due to the influence of the curved area where the propagation direction changes a lot. Surge propagation in the curved area occurs more obviously under wave superposition effect, and the number of wave peaks decreases. To analyze the graph of surge wave height at each testing point, it is easy to know that the maximum wave height at some testing points may not be the head wave crest. In order to verify its regularity, five groups of testing data at each testing point of the maximum wave height and the head wave crest are shown in Table 5:  
From the analysis of the data in Table 5, it can be seen that the maximum wave height and the head wave crest are basically the same at the testing points in the similar area, while for the diversion area and the curved area, the maximum wave height does not appear in the head wave. Some of them appear in the second wave or even the third one; this is due to the superposition effect of the wave reflection, which is more significant in the two areas. When the landslide volume is small, the head wave crest may be the maximum wave height of the testing points; however, when the volume is more than 3 m\({}^{3}\), the head wave crest is not the maximum wave height. This phenomenon is involved with the full submerge time. The full submerge time was calculated from the video, and the results are shown in Table 6. Based on the data in Table 6, it can be found that with the increase in volume, the landslide full submerge time also increases. When the time is greater than the wavelength, the subsequent volume into the water affects the wave height of the second wave of the surge, so the maximum wave height is not necessarily the head wave crest. Figure 7: Course characteristic factors and head wave crest at testing points. For this project, when studying the impacts of landslide surge disasters on a river course with diversion or curved areas, not only the disaster risk of the head wave, but also the maximum wave height that occurs in the subsequent wave column should be considered.
# An Optimized Load Balance Solution for Multi-homed Host in Heterogeneous Wireless Networks
## 3 Proposed System
### Distribution of Applications Data Over Available Multiple Connections  
The MADM algorithm used to provide the distribution of the application's data over available multiple connections described by the pseudo-code represented in Algorithm 1. In the algorithm, the \(k\) network connections are evaluated and verified, we determine the application data that are distributed over the \(k\) connections with the aid of the utility connection equation. The application traffic is classified and analyzed according to the MADM process.  
```
(1) Initialization A: given k interfaces Ik, 1 < k < K B: given file size N bytes C: given A is the comparison matrix of size n*n D: w is the eigenvector of size n*1 E: \(\Delta\)max is the eigenvalue, \(\Delta\)max \(\epsilon\) R > n. (2) Root of sum-product RSP = \((\sum_{k=0}^{n}\prod_{j=1}^{n}A)\)?1/n (3) Weight vector = [ \(\prod_{j=1}^{n}A\) * (1/n) / RSP (4) \(\Delta\)max = \(\sum\)( \((\sum_{j=1}^{n}A\) * Weight Vector) (5) CI = (\(\Delta\)max - n )/(n-1) (6) If CR > 0.1, then (7) Go to (2) Else (9) w = weight vector (10) \(\sum_{j=1}^{4}\alpha\ *wj=1\)
```  
**Algorithm 1**Application based MADM Process.  
Figure 5: Relationship between the proposed system model and the TCP/IP model.  
The numerical calculations of the selected MADM method are explained in detail within the model simulation and analysis section along with a verification of the proposed system for sending applications data per available multiple connections.
## 4 Results and Discussion
### Minimum Temperature  
Starting with the MBE, we see a span from 0.773 (MIROC5) to 0.914 (CMCC-CESM), an indication of the diverse ability of models to control systematic discrepancies. Further inspection shows models such as ACCESS1.3 and CNRM-CM5 as being not far behind, with MBEs of 0.87 and 0.846, respectively, illustrating the differing propensity towards over or underestimation across models. As for MAE, the variation extends from 1.757 (INM-CM4) to 2.181 (HadCM3), revealing the wide spectrum of absolute errors across the models. In light of this, the GFDL-CM3 model scores an MAE of 1.792, only slightly more than the best-performing INM-CM4 model, whereas the HadCM3 model lags behind, showing higher absolute errors.  
Concerning RMSE, values fluctuate between 6.771 (MRI-CGCM3) and 7.074 (HADG-EM2-ES), suggesting disparate levels of model accuracy. Notably, the CNRM-CM5 and MRI-ESM1 models have RMSEs of 6.895 and 6.944, respectively, aligning them more closely with the highest accuracy model, MRI-CGCM3.  
The NSE, another telling metric, sees the highest performance from the MRI-CGCM3 model at 0.229, with the lowest at 0.159 from the HADG-EM2-ES model. Here, the MRI-ESM1 model performs admirably with an NSE of 0.189, trailing behind the MPI-ESM-P model, which shows a significantly higher value. Lastly, the correlation coefficients range  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **MBE** & **MAE** & **RMSE** & ** NSE** & **Correlation Coefficient** \\ \hline ACCESS1.3 & 0.959 & 2.358 & 7.806 & 0.224 & 0.486 \\ CMCC-CESM & 1.092 & 2.498 & 7.783 & 0.229 & 0.494 \\ CNRM-CM5 & 1.018 & 2.428 & 7.768 & 0.232 & 0.495 \\ GFDL-CM3 & 0.984 & 2.353 & 7.759 & 0.234 & 0.496 \\ HadCM3 & 1.013 & 2.982 & 7.941 & 0.197 & 0.466 \\ HADGEM2-ES & 1.005 & 2.976 & 8.022 & 0.181 & 0.45 \\ INM-CM4 & 0.993 & 2.308 & 7.659 & 0.253 & 0.515 \\ MIRO5 & 0.926 & 2.425 & 7.772 & 0.231 & 0.492 \\ MPI-ESM-P & 1.029 & 2.277 & 7.715 & 0.242 & 0.506 \\ MRI-CGCM3 & 1.034 & 2.64 & 7.855 & 0.215 & 0.479 \\ MRI-ESM1 & 0.978 & 2.506 & 7.791 & 0.227 & 0.49 \\ NorESM1\_M & 0.968 & 2.485 & 7.834 & 0.219 & 0.481 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical summary of the climate models for maximum temperature.  
from 0.417 (HADG-EM2-ES) to 0.497 (MRI-CGCM3), reflecting the varying strengths of linear relationships between the predicted and observed values. Here, the INM-CM4 model shows a strong correlation of 0.462, while the NorESM1_M model shows a somewhat weaker correlation of 0.444.  
This assessment elucidates the nuanced performance characteristics across different climate models in the context of minimum temperature prediction. By highlighting these disparities, we enhance our understanding of the unique strengths and limitations of each model, thereby aiding in the more effective utilization of climate prediction tools.
## 3 Comparative Analysis of Existing Evaluation Methods
### Existing Evaluation Methods  
The Bayesian probability method leverages Bayes' theorem to calculate the posterior probability of an event, given prior information. It offers several advantages. It integrates prior knowledge with observed data, providing reliable inferences even with limited or incomplete data. It represents parameter or model uncertainty using probability distributions, effectively managing uncertainties. It possesses a solid theoretical foundation and delivers intuitive results. This paper employs the methods from [PERSON] et al. [21], [PERSON] et al. [35], and [PERSON] et al. [15] as examples to illustrate the \(V_{s}\)-based liquefaction assessment models derived from the Bayesian probability method.  
Figure 1: Sample distribution in Database A. (**a**) Sample distribution of liquefaction conditions; and (**b**) sample distribution under different fines contents.  
Figure 2: Sample distribution in Database B. (**a**) Sample distribution of liquefaction conditions; and (**b**) sample distribution under different fines contents.  
Logistic regression is a generalized linear regression analysis model predicated on the sigmoid function. Due to its efficiency, ease of understanding, and simplicity of calculation, it is widely utilized in binary classification and probability prediction. Compared to other generalized linear regression models, logistic regression exhibits a superior performance and greater applicability in seismic liquefaction assessments. This paper employs the methods from [PERSON] et al. (2016), [PERSON] et al. (2018), and [PERSON] et al. (2018) to illustrate the \(V_{s}\)-based liquefaction assessment models derived from the logistic regression method.
# Rock Breaking Performance of TBM Disc Cutter Assisted by High-Pressure Water Jet
## 3 Setup for Numerical Simulation
### Geometrical Model Subsection  
Based on the above modeling method, a geometrical model of rock broken by the disc cutter and the water jet has been established as shown in Figure 3. As the only part to touch and break the rock, a cutter ring was regarded as the disc cutter in this numerical simulation. The geometrical model of the disc cutter ring was built in accordance with the common 17 inches single-edge disc cutter, the outer ring diameter of which is 432 mm and the width of the disc cutter contacting the rock is 20 mm. A hexahedral mesh method was used for dividing the disc cutter geometrical model into 34,950 meshes. The geometry of the rock is a \(100\times 80\times 40\) mm cuboid, which is divided into 120,000 meshes. No constraints are set on the top surface of the rock, while the other surfaces are set as non-reflecting surfaces to simulate rock with an infinite volume. The water jet is set as a \(\Phi 1\times 5000\) mm cylinder filling with 320 layers of SPH particles. In the enlarged view of Figure 3, the disc cutter is set to transparency, so that the geometry of the water jet can be seen clearly.  
In practice, the water jet must be set at the front of the disc cutter, which is different from the position of the water jet relative to the disc cutter showed in Figure 3. Numerical simulation according to the actual situation will spend a lot of computation time. Therefore, to save the numerical resources, the position of the water jet is changed to go through the disc cutter, and the only setting that needs to be canceled in the numerical simulation is that of the contact between the water jet and disc cutter.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parts** & \(\rho\) **(kg/m\({}^{3}\))** & \(K\) **(GPa)** & **Poisson Ratio** & \(\sigma\) **(MPa)** \\ \hline Rock & 2500 & 33.1 & 0.22 & 28.5 \\ Disc cutter & 9500 & 3200 & 0.3 & / \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material parameters of rock.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameters** & \(\rho_{0}\) **(kg/m\({}^{3}\))** & \(\gamma_{0}\) **(m/s)** & \(C\) & \(S_{1}\) & \(S_{2}\) & \(S_{3}\) & \(E_{0}\) & \(\alpha\) \\ \hline Value & 1000 & 0.35 & 1647 & 1.921 & \(-\)0.096 & 0 & \(2.86\times 10^{-6}\) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material parameters of the water jet.
# Structure and Sound Absorption Properties of Spiral Vane Electrospun PVA/PEO Nanofiber Membranes
## 1 Introduction  
Noise pollution is becoming an increasingly valued issue in the world [1; 2; 3]. Noise does harm to human health and reduces the service life of objects [4; 5; 6]. Controlling the sound source and using sound-absorbing materials reasonably are considered effective ways to reduce noise [7; 8].  
The traditional sound absorption materials have good sound absorption properties at high frequency, while the sound absorption effect at low frequency range is relatively poor [9; 10; 11; 12]. In recent years, a variety of light non-woven materials were widely used for reducing noise [13; 14; 15]. The poor sound absorption performance of traditional sound-absorbing materials, at low-middle frequency was replenished [16; 17]. According to different sound absorption principles, sound-absorbing materials can be generally divided into resonant sound-absorbing structure and porous sound-absorbing structure [18; 19; 20]. Sound waves entered into the resonance-absorbing structures resonating within the structure, thereby dissipating large amounts of acoustic energy [21]. Lots of sound waves can enter into the porous materials, then the acoustic energy can be turned into heat or mechanical energy consumed, because of the internal friction, heat conduction, and relaxation between the sound waves and fibers [22; 23].  
Many sorts of polymers are electrospun into nanofiber membranes. Polyvinyl alcohol (PVA) can completely dissolve in heated water, film easily with well mechanical property, and has attracted intense attention [24; 25; 26]. Polyethylene oxide (PEO) belongs to semicrystalline polymer that contains amorphous regions and crystalline phases at room temperature. It dissolves in water at room temperature completely and is widely used because of its high thermal stability and strength [27; 28]. From the point of the environment, it is considered important that Polyvinyl alcohol (PVA) andPolyethylene oxide (PEO) are both water-soluble polymers [29]. Blending is the most straightforward and easy way to get new materials or improve the performance of polymers. Blended polymers generally exhibit superior performance compared to single-component polymer [30; 31].  
The main purpose of this work is to attain better sound absorption property by modifying PVA nanofibers membranes. With different contents of PEO, morphology of fibers, surfaces, and internal structures of membranes were changed respectively. As a result, the sound absorption performance of blend nanofiber membranes is improved greatly.
## 4 Experiment and Analysis
### Evaluation Metrics  
Two metrics were selected for the experiments to evaluate the compared methods. P@20 (precision) is widely used as a measure of prediction accuracy, which represents the proportion of correctly recommended items among the top 20 items. MRR@20 (mean reciprocal rank) is the average of the reciprocal rank of correctly recommended items, where a larger MRR value represents a correct recommendation at the top of the ranking list.  
In order to verify the performance of the model, two groups of experiments are designed in this paper, and the experimental results are analyzed. The first set of comparative experiments compares the performance of SE-GNNRM with nine baseline models. The results of the comparative experiments are shown in Table 2. The second set is hyperparameter experiments to investigate the effect of the co-occurrence parameter \(\phi_{ij}\) on model performance.
# Tensor-Based Algorithms for Image Classification
## 2 Prerequisites
### SINDy  
SINDy [11] was originally developed to learn the governing equations of dynamical systems from data. We will show how it can, in the same way, be used for classification problems. Consider an autonomous ordinary differential equation of the form \(\dot{x}=f(x)\), with \(f\colon\mathbb{R}^{d}\to\mathbb{R}^{d}\). Given \(m\) measurements of the state of the system, denoted by \(x^{(j)}\), \(j=1,\ldots,m\), and the corresponding time derivatives \(y^{(j)}:=\dot{x}^{(j)}\), the goal is to reconstruct the function \(f\) from the measurement data. Let \(X=[x^{(1)},\ldots,x^{(m)}]\in\mathbb{R}^{d\times m}\) and \(Y=[y^{(1)},\ldots,y^{(m)}]\in\mathbb{R}^{d\times m}\). That is, \(d^{\prime}=d\) in this case. To represent \(f\), we select a vector-valued basis function \(\Psi\colon\mathbb{R}^{d}\to\mathbb{R}^{n}\) and define the transformed data matrix \(\Psi_{X}\in\mathbb{R}^{n\times m}\) by  
\[\Psi_{X}=\left[\Psi(x^{(1)})\quad\ldots\quad\Psi(x^{(m)})\right]. \tag{2}\]  
Omitting sparsity constraints, SINDy then boils down to solving  
\[\min_{\Xi}\left\|Y-\Xi^{\top}\Psi_{X}\right\|_{F}, \tag{3}\]  
where  
\[\Xi=\left[\xi_{1}\quad\ldots\quad\xi_{d}\right]\in\mathbb{R}^{n\times d} \tag{4}\]  
is the coefficient matrix. Each column vector \(\xi_{i}\) then represents a function \(f_{i}\), i.e.,  
\[y_{i}^{(j)}\approx f_{i}(x^{(j)})=\xi_{i}^{\top}\Psi(x^{(j)}). \tag{5}\]  
We thus obtain a model of the form \(\dot{x}=\Xi^{\top}\Psi(x)\), which approximates the possibly unknown dynamics. The solution of the minimization problem (3) with minimal Frobenius norm is given by  
\[\Xi^{\top}=Y\Psi_{X}^{+}, \tag{6}\]  
where \({}^{+}\) denotes the pseudoinverse, see [20].
## 2 Materials and Methods  
The modeling system comprised of the meteorological model WRF [10] and the photochemical model CAMx was applied over three nested domains covering Europe and North Africa (in 18 km), the Eastern Mediterranean (in 6 km), and the Greater Athens Area (GAA) (in 1.2 km). The simulations referred to cold and warm periods of the year 2019, i.e., January and July, respectively.  
Anthropogenic gaseous and particulate matter emissions were obtained from the most recent CAMS-REGv5 emissions database [11]. A detailed emission inventory for the GAA was prepared, including bottom-up emissions for the heating and road transportation (exhaust and non-exhaust) sectors calculated on the basis of activity data and WRF meteorology [12]. Natural emissions included in the simulations refer to sea salt, windblown dust, and biogenic NMVOC and were calculated using the natural emissions model NEMO [13; 14]. The WRF and CAMx models were driven by boundary conditions from the ERA5 reanalysis data and the CAMS-IFS global model [15], respectively.  
Sensitivity runs were performed and compared with CAMx using the SOAP2.2 and 1.5 DVBS schemes for organic gas-aerosol partitioning and oxidation [9]. Intermediate-volatility organic compounds (IVOC) emissions need to be provided in CAMx simulations with the VBS scheme, in addition to the traditional anthropogenic and biogenic NMVOC used by SOAP. IVOC are important SOA precursors and were assumed as 1.5 times of POA [4].  
The PM modeled results were evaluated against in situ surface measurements from (a) the monitoring network of the Ministry of Environment and Energy (MEEN) ([[https://ypen.gov.gr/](https://ypen.gov.gr/)]([https://ypen.gov.gr/](https://ypen.gov.gr/)), accessed on 24 February 2023) measuring PM\({}_{10}\) and PM\({}_{2.5}\) concentrations, (b) the PANACEA research monitoring network ([[https://panacea-ri.gr/](https://panacea-ri.gr/)]([https://panacea-ri.gr/](https://panacea-ri.gr/)), accessed on 24 February 2023) including, in Athens, two urban background stations (i.e., Thissio and Demokritos), operated, respectively, by the National Observatory of Athens (NOA) [16] and by the N.C.S.R. \"Demokritos\" [17], which provide PM\({}_{2.5}\) chemical components concentrations, and (c) the PANACEA monitoring network of low-cost PM\({}_{2.5}\) sensors ([[https://air-quality.gr/](https://air-quality.gr/)]([https://air-quality.gr/](https://air-quality.gr/)), accessed on 24 February 2023), which in the GAA has been operated by NOA since July 2019 [18]. For validation purposes, the following evaluation metrics were estimated: normalized mean bias (NMB), normalized mean square error (NMSE), index of agreement (IOA), factor of 2 (Fac2), fractional bias (Fb), and fractional standard deviation (Fs). Desert dust transported over the study area may represent a high share of the PM atmospheric load [19], so these days were excluded from the analysis.