esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The introduction of the decomposition algorithm can significantly improve the generalization ability of the neural network. When the trained combined model is applied to predict the settlement of other measurement points on the section, the prediction error of the model combined with the decomposition algorithm is significantly lower than that of the single neural network model.  
The improved VMD parameter optimization method can accurately determine the optimal combination of VMD parameters, thus effectively reducing the occurrence of modal confusion and ensuring the rapidity and effectiveness of the decomposition process. This method significantly improves the performance of the VMD algorithm by accurately characterizing the settling information and avoiding unnecessary noise interference. The optimization results not only improve the accuracy of the decomposition results but also enhance the accuracy of the model in settlement prediction, making it more robust and adaptable, thus providing more reliable support for settlement prediction.  
Compared with a single neural network prediction model, the combined prediction model based on modal decomposition significantly improves the prediction accuracy. The combined model containing the decomposition algorithm achieved high accuracy in the prediction process, with average reductions of 33% and 37% in the root mean square error and average absolute error, respectively. The combined forecasting model can more accurately capture the short-term fluctuation characteristics and limit the interference of abrupt data on long-term trend prediction, which enhances the practicality and adaptability of the model in practical applications.  
Q.S.: writing--review and editing, data curation, investigation, funding acquisition. Y.W.: writing--original draft, administration. L.W.: methodology, supervision. Q.C.: data curation, administration. Y.L.: methodology, supervision. Z.L.: writing--review and editing, funding acquisition. W.W.: visualization, data curation. F.L.: data curation, visualization. T.L.: writing--review and editing, visualization, data curation. J.S.: writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant nos. 51508556, 42172319, and 41771083), the China University of Mining and Technology-Beijing Inner Mongolia Research Institute Foundation (IMRI23009), the Program of China Scholarship Council (grant no. 202406430056), and the Fundamental Research Funds for the Central Universities (Ph.D. Top Innovative Talents Fund of CUMTB) (grant no. BBJ2024079).  
Not applicable.  
The dataset in this study is available upon request from the authors.  
Author [PERSON] was employed by the company Beijing Urban Design and Development Group Co. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Classification of Trash and Valuables with Machine Vision in Shared Cars
###### Abstract  
This study focused on the possibility of implementing a vision-based architecture to monitor and detect the presence of trash or valuables in shared cars. The system was introduced to take pictures of the rear seating area of a four-door passenger car. Image capture was performed with a stationary wide-angled camera unit, and image classification was conducted with a prediction model in a remote server. For classification, a convolutional neural network (CNN) in the form of a fine-tuned VGG16 model was developed. The CNN yielded an accuracy of 91.43% on a batch of 140 test images. To determine the correlation among the predictions, a confusion matrix was used, and in addition, for each predicted image, the certainty of the distinct output classes was examined. The execution time of the system, from capturing an image to displaying the results, ranged from 5.7 to 17.2 s. Misclassifications from the prediction model were observed in the results primarily due to the variation in ambient light levels and shadows within the images, which resulted in the target items lacking contrast with their neighbouring background. Developments pertaining to the modularity of the camera unit and expanding the dataset of training images are suggested for potential future research.  
vision-based; shared cars; prediction model; classification; convolutional neural network; camera module +
Footnote †: journal: Applied Sciences
# Epileptic Seizure Detection in Neonatal EEG Using a Multi-Band Graph Neural Network Model
## 3 Methods
### Multi-Band Graph Neural Network  
The architecture of the proposed multi-band graph neural network is shown in Figure 3. The front end of MBGNN is a multi-band graph convolution module with six channels for six sub-band graphs. Each route has a two-stack graph convolution and pooling structure consisting of GAT convolution layers and DIFFPOOL layers, where BN and ReLU stand for batch normalization layer and activation function layer, respectively. The back end of MBGNN is a successive chain of band-wise attention layer, linear layer, and softmax layer. Further details on model parameters are given in Table 2.  
The graph representations of the EEG epoch in each sub-band are separately fed to the graph convolution and graph pooling layers. Then, the band-wise attention layer is used to adaptively aggregate the high-level information of all bands. Finally, the aggregated information is forwarded to the softmax layer for classification. Details of the graph convolution, graph pooling and graph aggregation algorithms are given in the following subsections.
## 2 Impact Resistance Theories
### Cavity Expansion Analysis for Concrete  
Introduced by [PERSON] et al. [18], cavity expansion analysis was applied to solve the governing equations of the spherical cavity or cylindrical cavity expansion process in an elasto-plastic incompressible medium. Then [PERSON] and [PERSON] et al. [19] extended this to compressible material penetration problems. This classical model has been successfully applied to metal and concrete material penetration analyses.  
Projectile penetration inside a solid medium can be regarded as a spherically symmetric cavity expansion process. Embedded in an infinite and isotropic medium, the zero initial radius cavity expands at a constant velocity. Due to the instantaneous rise of dynamic pressure, spherical stress waves are generated to form different response regions corresponding to the concrete constitutive law. According to continuum mechanics, equations of mass conservation and momentum conservation [8] in Euler coordinate for compressible spherical cavity expansion analysis at time \(t\) are:  
\[\text{div}\;\sigma=\rho a \tag{1}\]  
\[\frac{\partial\rho}{\partial t}+\text{div}(\rho v)=0 \tag{2}\]  
where \(\sigma\) is the stress tensor, \(a\) is acceleration, and \(v\) is velocity. In a spherical coordinate system, the governing equations [9] can be expressed as:  
\[\frac{\partial\sigma_{\text{r}}}{\partial\tau}+\frac{2(\sigma_{\text{r}}- \sigma_{\text{\Earth}})}{r}=-\rho\bigg{(}\frac{\partial v}{\partial t}+v\frac{ \partial v}{\partial r}\bigg{)} \tag{3}\]  
\[\rho\bigg{(}\frac{\partial v}{\partial r}+\frac{2v}{r}\bigg{)}=-\bigg{(}\frac {\partial\rho}{\partial t}+v\frac{\partial\rho}{\partial r}\bigg{)} \tag{4}\]  
where \(r\) is the radial coordinate, and \(\sigma_{\text{r}}\) and \(\sigma_{\text{\Earth}}\) are radial and circumferential Cauchy stress.  
With the corresponding constitutive law for concrete materials, attempts have been made to numerically solve the governing equations with the Runge-Kutta method [20]. The normal stress \(\sigma_{\text{n}}\), referred as penetration resistance, acting on the projectile nose is usually expressed as:  
\[\sigma_{\text{n}}=\rho v^{2}+R \tag{5}\]  
where \(\rho v^{2}\) and \(R\) represent the inertial dynamic resistance and static resistance. [PERSON] and his coworkers [21; 22; 23] deemed the target strength parameter as \(R=Sf_{c}^{\prime}\) where \(S\) is a dimensionless constant and \(f_{c}^{\prime}\) is the unconfined compressive strength of concrete. After validation with extensive penetration data [21; 22; 24; 25; 26], the semipericial penetration model gives:  
\[S=82.6\bigg{(}f_{c}^{\prime}/10^{6}\bigg{)}^{-0.544} \tag{6}\]  
Figure 1: Diagram of a projectile penetrating a steel plate reinforced concrete wall.  
Recently, researchers have pointed out that the above penetration resistance equation only applies to medium caliber projectile cases (shank diameter ranging from 12.9 mm to 30.5 mm) [27]. To avoid the concrete size effect in penetration, this work focuses on a 1-inch (25.4 mm) diameter projectile meeting the application range.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 4 Interference Mitigation Schemes and Techniques by 3 GPP
### Interference Mitigation in D2D
#### 4.1.1 Resource Allocation  
Resource sharing in D2D underlay communications poses a high risk of cross-tier interference. 3 GPP proposes semi-static and dynamic scheduling approaches based on centralized or distributed architecture [170]. The BS may allocate orthogonal resources or allow reuse under SINR constraints to optimize SE while mitigating interference.
## 2 Related Work
Slightly different to other works, the authors of [26] extend routing to drones, leading to the so called flying ad-hoc network (FANET), again paying attention to energy consumption. However, only a few works have specifically used EMF exposure, or other related parameters as a routing parameter in the past. One of them is the one presented in [27]. In this work, the authors introduce a band selection scheme for D2D, where Nearest Neighbor Routing (NRR) is used as the routing solution. Nevertheless, this work differs from ours in various aspects. First, it does not propose a routing solution, but it exploits an existing one (NRR) and provides a band-selection scheme. In addition, the work presented in [27] addresses the routing problem from a theoretical perspective. On the other hand,we provide a whole framework for EMF routing, ranging from the metrics definition to a concrete protocol implementation. Based on a newly minted routing metric that considers the EMF exposure, in this work we define and evaluate a novel routing protocol, which is an extension of the ones described in [28; 29], where preliminary ideas and partial implementations were presented. Regarding legacy routing solutions, two main approaches exist for multi-hop wireless networks: (1) reactive, in which nodes trigger route discovery procedures only when they need to; and (2) proactive, where nodes periodically send topology-related information, which is afterwards used to establish the corresponding routes. One of the main advantages of the reactive protocols is that, especially for moderate traffic loads, they exhibit a better energy consumption behavior. Hence, since the main goal of this work is to reduce the exposure, the core functionality of one the most widespread reactive protocols, AODV [7], was exploited to develop the solution proposed herewith. The effect sought by the novel protocol is twofold: (1) a reduction of the overall exposure, closely related to the transmitted power; (2) the aim to avoid EMF hot-spots, thus copying the behavior of load-balancing solutions. The reader might refer to the survey by [PERSON] et al. [30] for a succinct discussion of the most interesting load balancing proposals.
# Robust DOA Estimation Using Multi-Scale Fusion Network with Attention Mask
## 1 Introduction
This enables the network to focus on the relevant information and shield the interference of reverberation and noise. 3. Experimental results on both simulated and real datasets demonstrate that our method has significantly enhanced the accuracy and stability of DOA estimation compared with other state-of-the-art methods. The rest of the paper is organized as follows. Related work is described in Section 2. Section 3 presents spherical harmonic analysis of signals. In Section 4, the proposed approach is introduced. Section 5 describes the experimental setup and discusses the results obtained from synthetic and real datasets. Finally, conclusions are drawn in Section 6.
## 3 Results and Discussion
### Stress Field on the Joint Plane  
To reveal the stress evolution on the 3D nonpersistent joint specimen during loading, both normal stress and shear stress were monitored by a measurement circle (Figure 15), and four points a, b, c, and d (which correspond with Figure 9) were recorded to analyse the stress state.  
During loading, the normal stress distribution in the 3D nonpersistent joint plane is characterized as shown in Figure 16. For the specimen with different inclinations, at the initial stage of loading (for the stress state in point a), stress concentration occurs in the rock bridge. However, it is worth noting that the stress concentration on the joint surface is relatively weak for specimens with inclinations of 75\({}^{\circ}\) and 90\({}^{\circ}\). For the specimen with inclination \(\theta=0^{\circ}\), before failure, the stress concentration area on the joint plane has little change (Figure 16a). The main reason is that the specimen experiences splitting failure, and all rock bridges play a supporting role under axial loading. For the specimen with \(\theta=75^{\circ}\), as the failure belongs to the shear mode, friction slip occurs along the joint plane during the postpeak stage. Then, a significant distinction in stress appears between rock bridges (Figure 16c). The normal stress of the rock bridge at \(\theta=90^{\circ}\) is virtually unchanged, and some rock bridges deform and rupture at the last load failure stage, resulting in localized stress concentration. The stress concentration mainly occurs at the upper and lower ends (Figure 16d).  
Figure 14: Microcrack proportion for jointed specimens with different inclinations and persistence.  
Figure 16: _Cont._  
Figure 15: Measurement circle distribution around the joint plane.  
Figure 17 shows the evolution of the shear stress on the 3D nonpersistent joint surface under uniaxial loading. For the specimens with \(\theta\) = 0\({}^{\circ}\) and 90\({}^{\circ}\), the variation in the stress concentration area is very slight (Figure 17a,d), which is attributed to the lack of slip behaviour along the joint surface during the fracture process of the specimens. Unlike the specimen with \(\theta\) = 0\({}^{\circ}\) and 90\({}^{\circ}\), the variation in the stress concentration area at 75\({}^{\circ}\) is the most significant, followed by that at 30\({}^{\circ}\). Regardless of the slip fracture mode or mixed fracture mode, slip will occur along the joint plane during specimen failure, so all rock bridges on the joint plane will not always play a supporting role in the entire loading process. Thus, after the failure of the specimen, the joint plane is staggered due to the slip, so the support area changes and the shear stress distribution area also changes accordingly (Figure 17b,c).  
Figure 16: The normal stress evolution of the 3D nonpersistent joint during loading.  
Figure 17: The shear stress evolution of the 3D nonpersistent joint during loading.
## 4 CS Detector Architecture  
There are numerous options for implementing a CS based detector system as discussed in [12, 13, 14]. Spatial light modulators are used to implement measurement matrices. Previous work by [15] show a CS implementation using coded aperture masks. In the literature from [16, 17, 18], they implemented a single pixel camera using liquid crystal displays (LCD). Their work shows that lensless single pixel cameras can be implemented using LCD as their coded apertures.  
In our analysis, we focus on using a DMD array for the CS projection. A CS architecture using a DMD array can have a frame rate of 32 KHz with 2048 \(\times\) 1080 pixels [13]. In our simulations, we show the effectiveness with 25% of \(n\). Let us assume \(n=2048\times 1040\) pixels. We would need 550,800 measurements. With the frame rate, this gives us 17.2 s to obtain one CS image, in addition to the needed exposure time. A typical microlensing event can last for 30 days. In [19], they discovered the shortest time scale microlensing event measured,  
Figure 4: Sample differenced image.  
Figure 3: Sample observed image.  
where \(t_{e}\approx 41.5\) min. Using Nyquist rate for this short time scale, we would expect to sample at least \(\frac{t_{e}}{2}\approx 20.75\) s. Detection efficiency will not only depend on the cadence of the system but also on the flux magnification and Field-of-view (FOV). The larger the FOV, the greater the chances of detection of a microlensing event.  
A CS detector system would be beneficial for use on Small Satellites, where data storage and downlink can be limiting factors. Using a CS detector system on a constellation of satellites, we can detect a microlensing parallax, as shown in Figure 5. In our simulations, we assume the number of satellites in the constellation to be [0, \(n_{sat}\)], where the maximum number is \(n_{sat}=8\).  
A detector concept for placing a telescope on a Small Satellite can be based off of ASTERIA (Arcsecond Space Telescope Enabling Research in Astrophysics) [20]. ASTERIA is a 6U CubeSat with a telescope aperture of 6.7 cm with a CMOS detector of 2592 \(\times\) 2192 pixels. For a CS system, our optics would include a telescope, micro-mirror arrays, or any spatial light modulators, as well as a photodiode to acquire the sum total of the reflected light from the micro-mirrors, as shown in Figure 6.
## 4 Discussion  
### Relationship between NIRv and GPP  
As the product of NDVI and NIR, NIRv has been proven to have strong and linear correlations with GPP in various ecosystems and different time scales [17; 18; 21]. [PERSON] et al. used NIRv to dynamically estimate GPP based on different land cover types and spatial distributions, and the seasonal patterns of their results were consistent with some mainstream GPP products [22]. However, due to the simple linear relationship between them, in theory, the GPP estimation based on NIRv should be highly dependent on LC (Figure 8c).  
Figure 7: Statistics for the changes in (**a**) NIRv and (**b**) GPP in the different land cover (LC) schemes, using the Theil–Sen estimator, by the sub-basins of the Yellow River basin (shown in Figure 1) from 2001 to 2018. The LC schemes include the static MODIS LC data in 2001 (in red), dynamic MODIS LC data (in green), and dynamic CCI LC data (in blow) in the panel (**b**). The red dots denote median values, the boxes cover the interquartile range, the thin lines reach the 5 th and 95 th percentiles, and the black points denote the outliers.  
Moreover, the multisource products' discrepancies would have an important impact on the estimation of GPP by NIRv (Figure 8b). These issues provided the basis and hypothesis for this study.  
The temporal and spatial variations in NIRv in the study area were highly consistent with that of GPP. The changes in GPP were apparently driven by the changes in NIRv. Meanwhile, the coefficients of the linear relationship between GPP and NIRv depend on land cover types [22]. In order to reflect the influence of LCC on GPP estimation, we designed dynamic and static schemes of LC. The results showed that even if NIRv was the same, the estimated GPP based on different land cover products was still quite different. Our results showed that the influence of LCC and DMP on the relationship between GPP and NIRv was obvious and important (Figure 2c,d and Figure 3b-d), and the trend of NIRv without considering land cover change was not a substantial GPP trend (Figure 6), which confirmed our hypothesis. [PERSON] et al. investigated the effects of LCC on the annual GPP trend over the conterminous United States and showed the influences were significant, which also supports our results [26].
## 2 Challenges for Modeling of Five-Axis Coordinate Measuring Systems
### Determination of Real Geometric Parameters of Probe Head Used in Five-Axis System  
Another important issue concerning the virtual machine for a five-axis measuring system is identification of real values of geometrical parameters that describe a probe head used in a five-axis system. It is obvious that dimensions given by probe head producers are nominal values and parameters of each probe head specimen will vary among themselves. Thus, the appropriate calibration procedure that will allow determination of real probe head geometric parameters have to be developed. There are few possible methods that may be used. Some of them are based on self-calibration techniques, others use measurements of calibrated length standard.  
In previous works, the authors solved similar problem related to modelling of the AACMM by measurement of point coordinates in different positions in machine measuring volume. For each measurement, the orientation of the probe as well as indications of encoders were saved. Then the values of geometrical parameters can be fitted so the values obtained through measurements meet with those given by the model. Another approach that may be used here is based on measurement of standard elements such as rings or spheres arranged relative to each other. The position of standards should be calibrated first and then, by measuring them on a five-axis measuring system, the difference between the nominal element position obtained during calibration and measurement result can be calculated. The problems connected to this approach include proper design of standard arrangement and estimation of the influence of a touch-trigger probe on the measurement results. This is the reason why the authors are more likely to use the first of mentioned methods.  
After determination of real geometric parameters of the probe head, they should be used in a kinematic model of a whole five-axis coordinate measuring system. This model could be used to simulate the _x,y,z_ coordinates of points included in the considered measurement. As an input quantity for this simulation, the indications from three linear scales of the machine and two encoders of the probe head should be used. They should be recorded for all measuring points during a single run of considered measurement. The described kinematic model is the base of the virtual model of a five-axis coordinate measuring system and should be extended in order to take into account all identified influencing sources including the probe head errors model presented in [1], error sources pointed out in Section 2.3 of this paper, and any other sources that could significantly influence the uncertainty of measurements performed using five-axis measuring systems.