esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Recent Research into the Causes and Control of Noise during High-Speed Train Movement
###### Abstract  
Since the invention of the train, the problem of train noise has been a constraint on the development of trains. With increases in train speed, the main noise from high-speed trains has changed from rolling noise to aerodynamic noise, and the noise level and noise frequency range have also changed significantly. This paper provides a comprehensive overview of recent advances in the development of high-speed train noise. Firstly, the train noise composition is summarized; next, the main research methods for train noise, which include real high-speed train noise tests, wind tunnel tests, and numerical simulations, are reviewed and discussed. We also discuss the current methods of noise reduction for trains and summarize the progress in current research and the limitations of train body panels and railroad sound barrier technology. Finally, the article introduces the development and potential future applications of acoustic metamaterials and proposes application scenarios of acoustic metamaterials for the specific needs of railroad sound barriers and train car bodies. This synopsis provides a useful platform for researchers and engineers to cope with problems of future high-speed rail noise in the future.  
high-speed train; noise control; acoustic metamaterial; sound barrier; rail vehicle +
Footnote †: journal:  
00 2022127508  
Shinkansen in 1975. The values specified in decibels (LPA.Smax) in these standards are 70 dB or less, mainly for residential areas, and 75 dB or less for other areas [7].  
The rise in the industrial civilization of the nineteenth century brought the railroad, and people's pursuit of train speed drove the continuous development of train technology. Steam locomotives reached their peak in the 1930s when Gresley's streamlined \"Mallard\" locomotive reached a top speed of 202 km/h. In 1964, Japan's Shinkansen was the first to commercialize modern electric high-speed trains, operating at 210 km/h en route from Tokyo to Osaka.  
Nowadays, the operating speed of high-speed trains has reached 350 km/h. In the future, the speed of high-speed trains will be further increased, and the noise problem will be further exacerbated, making the train noise problem a hot research topic among engineers with an interest in rail transportation. High-speed train noise is already a problem that cannot be ignored, and if this problem is not solved timeously, it will seriously affect the future development of high-speed trains.  
To this end, this study presents a selective literature review focusing on:  
1. The causes of high-speed train noise and the distribution of the sound sources;
2. The current main research methods for high-speed train noise;
3. Traditional methods of high-speed train noise control;
4. Potential uses of acoustic metamaterials in the area of high-speed train noise.
# Individualized Interaural Feature Learning and Personalized Binaural Localization Model +
## 2 Individualized Feature Selection Using Mutual Information
### Mutual Information Computation  
As one of the most common measures to evaluate the dependency between variables, MI is widely used to estimate the maximally relevant feature selection that corresponds to a particular outcome [26; 27]. The MI \(\mathcal{I}\) of two discrete variables, \(x\) and \(y\), is defined as:  
\[\mathcal{I}(x;y)=\sum_{x}\sum_{y}P(x,y)\log\frac{P(x,y)}{P(x)P(y)}, \tag{5}\]where \(P(x)\) and \(P(y)\) represent the joint probability and marginal probabilities of variable \(x\) and \(y\), respectively. Similarly, the MI between an interaural feature \(v_{\mu}\) and \((\theta,\varphi)\) is given by:  
\[\begin{split}\mathcal{I}_{\theta}(v_{\mu};\theta)&= \sum_{v_{\mu}}\sum_{\theta}P(v_{\mu},\theta)\log\frac{P(v_{\mu},\theta)}{P(v_{ \mu})P(\theta)}\\ \mathcal{I}_{\varphi}(v_{\mu};\varphi)&=\sum_{v_{ \mu}}\sum_{\varphi}P(v_{\mu},\varphi)\log\frac{P(v_{\mu},\varphi)}{P(v_{\mu}) P(\varphi)}\end{split} \tag{6}\]  
We now focus on the interaural phase and magnitude spectra feature analysis for elevation localization, since the spectra information is the major cue to determine the elevation [12]. The dependency between spectra feature and azimuth is ommited, because the localization of azimuth relying on the signal arriving time and level difference between two ears is comparably easier and more robust in an interaural-polar system [24].  
In the elevation dependency analysis, the training dataset is denoted as \(\{\mathbf{v}^{(n)},\varphi^{(n)}\}_{n=1}^{N}\), where \(n=1,...,N\) represents the training data index. Based on consideration of computation complexity, the probability components \(P(v_{\mu},\varphi)\), \(P(v_{\mu})\) and \(P(\varphi)\) are estimated by histogram-based probability estimation [28] as:  
\[\begin{split} P(v_{\mu},\varphi)&=\frac{1}{N}\sum_{n =1}^{N}z(v_{\mu}^{(n)}=v_{\mu},\varphi)\\ P(v_{\mu})&=\frac{1}{N}\sum_{n=1}^{N}z(v_{\mu}^{(n) }=v_{\mu})\\ P(\varphi)&=\frac{1}{N}\sum_{n=1}^{N}z(\varphi^{(n) }=\varphi)\end{split} \tag{7}\]  
where the indicator function \(z(\mathbf{Z})\) with event \(\mathbf{Z}\) is defined as:  
\[z(\mathbf{Z})=\begin{cases}1&\mathbf{Z}\text{ is true,}\\ 0&\text{otherwise.}\end{cases} \tag{8}\]  
Therefore, the MI between each feature element \(v_{\mu}\) from the vector \(\mathbf{v}\) can be obtained by (7). Then, by traversing all the elements in vector \(\mathbf{v}\), the MI vector \(\mathcal{I}(\mathbf{v};\varphi)\) is defined as:  
\[\mathcal{I}(\mathbf{v};\varphi)=[\mathcal{I}(v_{1};\varphi),...,\mathcal{I}( v_{\mu};\varphi)] \tag{9}\]  
Now in order to evaluate the dependency between elevation \(\beta\) and interaural cues \(v_{\mu}\) only and omit the impact of azimuth, the mutual information is calculated on each sagittal plane separately and the mutual information vector for each sagittal plane is denoted as \(\mathcal{I}_{\alpha}(\mathbf{v};\varphi)\).
## 3 Methods to Address the Pipeline of End-To-End ADS  
This section will discuss the latest literature methods to address the different stages that constitute an end-to-end ADS. This section is divided into four main categories. The first one presents image pre-processing and instance segmentation, in which the main aim is to locate all legal boundaries and other objects of reference. It is inferred that although one vehicle is physically capable of driving on a sidewalk, it is not correct for it to do so, and therefore the ADS needs to recognize the road lanes. Nonetheless, not all road lanes are used for driving in the required direction (i.e., the single direction only, cycling/pedestrian lanes). Thus, this step plays a crucial role in detecting which road lanes shall the vehicle occupy. The second one focuses on object detection within the semantic segmenting boundaries of the route. For instance, algorithms such as YOLO are used to recognize each object, and this data is fused with LiDAR point clouds using an MV3D-like architecture [12]. Processes such as these help the vehicle draw prism points on each detected object and know where its center of mass relies upon and the volume being handled within the lane space. Human and sentiment detection techniques (for driver, passengers, and pedestrians) are discussed in the third section. Finally, in the fourth category, cloud computing and parallelization are discussed to speed up and improve the pipeline. For instance, these techniques can train all object detection models, communication of different vehicles, etc.  
Figure 14: Frenet frame visualization (image under creative commons license).
# User-Cell Association for Security and Energy Efficiency in Ultra-Dense Heterogeneous Networks
###### Abstract  
The last decades have been characterized by an exponential increase in digital services. The demand is foreseen to further increase in the next years, and mobile networks will have to mandatorily supply connections to enable digital services with very different requirements, from ultra high speed to ultra low latency. The deployment and the coexistence of cells of different size, from femto to macro, will be one of the key elements for providing such pervasive wireless connection: the ultra dense networks (UDN) paradigm. How to associate users and base stations is one of the most investigated research topics. Many criteria can be drawn, from minimization of power consumption to optimization of throughput. In this paper we propose a new utility to optimize two of the most important features of future mobile connection: security and energy consumption. By using our utility it is possible to jointly select the base station to be activated in a UDN, and associate users to the base stations with the aim of maximizing the secure throughput by spending the minimum energy. Moreover, we propose a heuristic that allows to achieve performance very close to the optimal one with reduced complexity. Effectiveness of the proposed approach is proved by means of comparison with benchmark approaches.  
physical-layer security; ultra dense networks; energy efficiency +
Footnote †: journal: Sensors
# Neural Network-Based Reference Block Quality Enhancement for Motion Compensation Prediction
## 3 Proposed Method
### An RBENN Integrated in H.266/VVC  
In the configuration file of H.266/VVC, CU sizes range from \(4\times 4\) to \(64\times 64\). As the variation of CUs with small sizes is usually not large and the number of pixels is also small, an RBENN only enhances the CUs that are larger than \(16\times 16\). We trained three networks,denoted as RBENN 1, RBENN 2, and RBENN 3, to process CU blocks with minimum side lengths of 16, 32, and 64, respectively. Specifically, RBENN1 enhances CUs with sizes of \(\{16\times 16\), \(32\times 16\), \(16\times 32\), \(16\times 64\), \(64\times 16\)}, RBENN2 enhances CUs with sizes of \(\{32\times 32\), \(64\times 32\), \(32\times 64\)}, and RBENN3 enhances CUs with a size of \(\{64\times 64\}\). Figure 4 depicts the system diagram of the encoder and decoder when the neural networks are integrated into the H1.266/VVC. On the encoder side, the current CU finds the reference CU based on ME. An RBENN enhances the reference CU and replaces the original reference CU with its output. At the decoder, the current CU finds the reference CU in the reconstructed frame based on the decoded MV. The output of the RBENN is then filtered by a DCTIF to obtain the predicted CU.  
Figure 4(a) shows the process of integrating an RBENN into conventional motion compensation, while Figure 4(b) is the process of integrating an RBENN into affine motion compensation. In conventional motion compensation, the H.266/VVC standard finds the reference CU from the current CU according to the MV. First, the reference CU is expanded by 4 pixels, then a DCTIF is applied to generate the predicted CU. The residual CU is obtained by subtracting the predicted CU from the original CU. It is then transformed and quantized. Our method was designed to input a padded image patch into the proposed RBENN, and the output image patch is then interpolated and filtered by a DCTIF. In affine motion compensation, H.266/VVC splits the current CU into several \(4\times 4\) sub-CUs and calculates the MV of each sub-CU according to four-parameter or six-parameter affine models. The reference sub-CUs are then found through the corresponding MVs. By performing padding and fractional interpolation on the reference sub-CUs, the final predicted sub-CUs can be obtained. Finally, all the predicted sub-CUs are combined to obtain the predicted CU. In our method, we first obtain the minimum region containing all the reference sub-CUs and then pad the region and feed it to the neural network. Based on the corresponding position of the output image patch, the padded sub-CUs can be filtered by a DCTIF to obtain the predicted sub-CUs.  
Figure 4: A diagram of RBENN integrated into H.266/VVC.
## 3 Results and Discussion  
The FSRS technology has been successfully applied to a number of important photosensitive molecular systems including rhodopsin [27], bacteriorhodopsin [28], phytochrome [29], organic dyes in solar cells [30], Fe(II) spin crossover in solution [31], fluorescent proteins [7, 32, 33], and calcium-ion-sensing protein biosensors [34, 35, 36]. The main goal of this work is to construct a versatile, tunable FSRS setup that extends the wavelength detection window to the UV regime with desired resonance Raman enhancement. As a result, a wider range of photochemical reaction pathways can be  
Figure 1: Schematic of the UV-Femtosecond stimulated Raman spectroscopy (FSRS) experimental setup. The fundamental laser output is split to separately pump a home-built second harmonic bandwidth compressor (SHBC, light blue shaded area) and a broadband up-converted multicolor array (BUMA, light violet shaded area) based on the unique sum-frequency-generation-based (SFG-based) cascaded four-wave mixing (CFWM) in a thin BBO crystal. A photograph of SFG-CFWM sideband signals on a sheet of white paper is shown with the first sideband on either side of SFG highlighted by black dotted circles. The first sideband either on the FP side (S\({}_{\text{+1}}\)) or on the WL side (S\({}_{\text{-1}}\)) is used as the Raman probe pulse in conjunction with the narrowband SHBC output as the Raman pump to record anti-Stokes and Stokes stimulated Raman spectrum, respectively. The 20:80 BS represents 20% Reflection and 80% Transmission.  
elucidated particularly for metal-organic complexes in solution (absorption peak below 300 nm) and tyrosine residues in proteins (max absorption at \(\sim\)276 nm) in conjunction with a femtosecond actinic pump pulse. Because FSRS is a stimulated Raman technique, the concomitant generation of a pair of ps-Raman-pump and fs-Raman-probe pulses is required.
# Efficient Removal Of U(VI) Ions from Aqueous Solutions by Tannic Acid/Graphene Oxide Composites
###### Abstract  
Tannic acid/graphene oxide (TA/GO) composites were prepared in the present research, and their properties and sorption performance were evaluated by corresponding characterization methods and bath sorption experiments, respectively. The applications of TA/GO to remove U(VI) from aqueous solution were investigated with the maximum adsorption capacity of 87.8 mg\(\cdot\)g\({}^{-1}\) at low pH (pH = 3.6 \(\pm\) 0.03). The sorption of U(VI) ions on TA/GO followed the Langmuir model because of the complexation of oxygen-containing functional groups on the surface of TA/GO composites and uranium ions. TA/GO manifested excellent selective adsorption toward uranium ions with other metal ions (Cs\({}^{+}\), Sr\({}^{2+}\), Co\({}^{2+}\)). Furthermore, TA/GO as an effective adsorbent was reused to remove a large amount of U(VI) ions from aqueous solution. Therefore, TA/GO is an ideal material to remove highly toxic U(VI) ions from wastewater.  
tannic acid; graphene oxide; uranium; adsorption Article
## 5 Conclusions  
In this study, a numerical model is established for the uniaxial compression of laminated shale formation. Based on this model, the impacts of the bedding strength ratio (JSR) and shear-to-tensile strength ratio (STR) of beddings on the uniaxial compressive strength (UCS), the percentage of different crack types and the crack fractal dimension of the shale specimen have been investigated at different dip angles. Particularly, the percentage of each crack type, their transformation and the micro-cracking morphology are analyzed in detail. From these simulations and analyses, the following understandings and conclusions can be drawn.  
First, the numerical model established in PFC\({}^{\text{2D}}\) can well simulate the stress-strain responses and crack growth process of shale under uniaxial compression loadings. It can successfully mimic four failure modes and their transformation in beddings and the matrix. Therefore, this numerical model can be used to simulate the mechanical characteristics and micro-cracking behaviors of layered rock materials.  
Second, the bedding shear strength plays a decisive role in the UCS when the shear failure of bedding is dominant. Under this circumstance, the bedding shear strength is the essential index to change the UCS of shale. It is also found that the growth rate of UCS remains consistent with the increase in bedding strength.  
Third, the dip angle plays a decisive role in the morphology of main fractures under uniaxial compression loadings, and bedding strength parameters can change the percentage of crack types and the complexity of sub-fractures in the shale formation. Therefore, the fracture morphology in shale formation depends on the local distribution of stress and strength.  
Finally, micro-cracking morphology varies with the combination of tensile strength and shear strength of beddings. Their combination plays a different role in the percentage of matrix cracks and tensile cracks. A higher JSR can generate more matrix cracks. Comparatively, a higher STR may initiate more tensile cracks. The complexity of the micro-cracking morphology of shale may increase rapidly if the STR of beddings increases to some extent.  
Numerical simulation and writing, [PERSON] and J.W.; Editing, C.L. All authors have read and agreed to the published version of the manuscript.  
The authors are grateful for the financial support from the National Natural Science Foundation of China (Grant No. 51674246).  
The authors declare no conflict of interest.
# Multi-View Ground-Based Cloud Recognition by Transferring Deep Visual Information
## 3 Approach  
### Part Summing Maps  
With the appearance of large-scale image datasets and the development of high-performance computing systems, CNNs have shown promising performance in image classification [34] and object detection [35; 36]. Hence, we extract features from a CNN model to describe cloud images. Generally, an effective CNN requires a large number of training images. When there are insufficient training images to train a CNN, it results in overfitting. In this tribulation, we fine-tune the VGG-19 model [17] on our cloud datasets to train a CNN. As presented in Table 1, the VGG-19 model consists of 16 convolutional layers and three fully-connected (FC) layers. The size of receipt fields throughout the whole model is set to \(3\times 3\) pixels, and the number of receipt fields is different for each convolutional layer. In the process of fine-tuning the VGG-19 model, we replace the number of kernels in the final FC layer with the number of cloud categories.  
A lot of processes have been developed in utilizing feature maps for image representations in computer vision fields [37; 38; 39]. Furthermore, the feature maps for a convolutional layer describe different patterns. To obtain completed information from the convolutional layer, we propose PSMs based on all feature maps for image representations. Practically, we divide all feature maps from one convolutional layer into several parts for one cloud image evenly. Suppose that there are \(K\) parts of feature maps, as shown in Figure 3. Then we add the feature maps of each part into one part summing map (PSM), denoted as \(C^{k}\) (\(k=1,2,...,K\)), and it is formulated as:  
\[C^{k}=\sum_{j=(k-1)J+1}^{kJ}c_{j}^{k} \tag{1}\]  
where \(c_{j}^{k}\) indicates the \(j\)-th feature map and \(J\) is the number of the feature maps in each part.
# Attention V-Net: A Modified V-Net Architecture for Left Atrial Segmentation
## 2 Related Work  
### Left Atrial Segmentation  
#### 2.1.1 Supervised Segmentation  
In 2015, a full convolution semantic segmentation network [6] achieved excellent segmentation results, laying the foundation for the application of deep learning in image segmentation. In recent years, many end-to-end segmentation techniques were developed in the field of medical imaging, and some early atrial segmentation algorithms [14; 15; 16], based on supervised learning, have shown good results. For example, the champion of the 2018 MICCAI Left Atrium Segmentation Challenge proposed a segmented network with two V-Nets [14]. The first is used to roughly locate the atrial center, it crops out a fixed size area according to the prediction results, the second network finely divides the parts cropped in the previous stage. [PERSON] et al. [15] proposed a robust adaptive framework, nnU-Met, based on 2D U-Net and 3D U-Net. It replaces the complex process of artificial optimization using a systematic approach based on explicit and interpretable heuristic rules. It can perform plug-and-play on a variety of datasets and achieve the same effect of the state-of-the-art methods. [PERSON] et al. [16] proposed a method to segment the left atrium and left ventricle simultaneously on the 3D MRI data of the heart. This method uses the traditional neighborhood-based method to track and superimpose the upper and lower slices. Then, it reconstructs the 3D model of the segmented left atrium and left ventricle according to the 2D format. These methods can improve the segmentation accuracy of atrial structure to a certain extent, but it is still difficult to solve the actual situation of medical image data with few labels and small samples. Therefore, the recent development of semi-supervised learning has resulted in changes to atrial segmentation algorithms.
## 4 Conclusions  
In this paper, a dual-band directional antenna has been designed and fabricated. Two symmetrical rectangular strips and two symmetrical F-shaped strips are introduced to obtain the lower and higher frequency bands, respectively. The printed antenna is excited by a coupling rectangular microstrip. Simulated and measured S\({}_{11}\) agree very well at the desired frequency bands. The directional antenna exhibits a stable directional radiation pattern with measured gain of 6.55 dBi at the lower frequency bands and 8.1 dBi at the higher frequency bands. The proposed dual-band directional antenna is a promising candidate for WLAN stations with characteristics of anti-interference, high radiation efficiency and high gain.  
Conceptualization, Y.D.; methodology, J.L. and G.L.; investigation, G.D.; writing--original draft preparation Y.D.; writing--review and editing Q.C. and G.L.; supervision and funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
This research work was partially funded by National Natural Science Foundation of China under Grant No. 61671330 and No. 61340049 the Science and Technology Department of Zhejiang Province under Grant No. LGG19F010009, and Wenzhou Municipal Science and Technology Program under Grant No. C20170005 and No.2018 ZG019.  
The data supporting this research article are available upon request to the corresponding author.  
The authors declare that there is no conflict of interest regarding the publication of this manuscript.