esa-sceva/satcom-chunk-collection huggingface dataset:
# Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication
## 2 Materials and Methods
### The Model of Short-Term Traffic State Prediction
#### 2.3.2 Spatial Feature Extraction  
An intersection is defined as the structure of the road network graph, and the GCN model is more sensitive to traffic spatial features. The GCN model can deduce its own  
Figure 6: Graphs of vehicular state at intersection.  
vertex information by using the information of the surrounding vertexes and its own original information. Therefore, in the graph of intersection, both vertex information and structure of graph should be integrated for consideration. The GCN model can learn not only the characteristics of vertexes automatically, but also the correlation information between any of two vertexes.  
The convolutional neural (CN) model is an operation which convolves on the graph by the Fourier transform. The advantage of the GCN model is to extract spatial features by the neighborhood information of vertexes. According to the corresponding changes of graph convolution in [30], the most important graph convolution can be obtained, as shown in Equation (4):  
\[g*h=U(\mathbf{u}^{T}g\mathbf{\cdot}\mathbf{u}^{T}h) \tag{4}\]  
where \(g\) is convolution kernel function, \(h\) is graph signal on each vertex, and \(h=[h(1),\ h(2),\ \ldots,h(n)]\). \(\mathbf{u}\) is not only the basis of Fourier transform, but also the eigenvector of Laplace matrix. In particular, the more complicated the traffic situation becomes, the higher the computational complexity when the Laplace matrix is decomposed.  
According to [31], the function \(f\) can be used Chebyshev polynomials for \(k\)-order approximation. The recursive function of Chebyshev polynomial is expressed as  
\[\begin{array}{c}T_{k}(x)=2 xT_{k-1}(x)-T_{k-2}(x)\\ T_{0}=1\\ T_{1}(x)=x\end{array} \tag{5}\]  
where \(k\) is the order of Chebyshev expansion.  
On the spectral graph, the approximate solution is carried out by Chebyshev polynomials, and the effect is equivalent to calculate the characteristic of each vertex by extracting the information of the \(0\)-\((k-1)\)-th order neighbors of each vertex in the graph. Therefore, the output of the \((l+1)\)-th layer \(H^{l+1}\) in GCN can be written as  
\[\mathbf{H}^{l+1}=\sigma(\widetilde{\mathbf{D}}^{\
icefrac{{1}}{{2}}}\widetilde{\mathbf{W} }\widetilde{\mathbf{D}}^{\
icefrac{{1}}{{2}}}\mathbf{H}^{(l)}\theta^{(l)}) \tag{6}\]  
where \(\widetilde{\mathbf{W}}=W+I_{N}\) is the matrix with added self-connections, \(\mathbf{I}_{N}\) is the identity matrix, \(\widetilde{\mathbf{D}}=\sum_{j}\widetilde{\mathbf{W}}_{ij}\) represents the degree matrix, \(\mathbf{H}^{(l)}\) is the output of the layer, \(\theta^{(l)}\) is the parameter of the layer, and \(\sigma(\cdot)\) represents the sigmoid function for a nonlinear model.
## 3 Proposed Research Methodology
### Convolutional Neural Networks (CNN)  
The deep learning CNN technique distinguishes between various elements in an image by requiring and prioritizing several features in the images. CNN is a multilayered feedforward neural network and one of the key concepts in the deep learning approach. CNN needs much less pre-processing than other categorization methods [45]. Modest filtering techniques can also be effective with proper training [46]. The pixels around a convolution layer's neurons determine their features' output. The perceptron is the area of pixel density where a neuron's response might be affected [47].  
A multi-layer convolutional neural network was designed in which a convolutional layer served as the start layer in the construction of an input MRI image, which informs the dimensions of every image. CNN model basic structure is shown in Figure 2. Convolution kernel entangled with the input layer was created after gathering every image in the same aspect [48]. It was possible to generate 32 convolutional filters with the assistance of three channel tensors, with each filter having a size of \(3\times 3\). The input layer, which is the top layer, has an image size of 224 \(\times\) 224 pixels. It is a mathematical technique to modify a feature map by performing a dot product of two matrices. One metric represents the kernel, and the other displays the original image's pixel intensity values. The kernel is moved vertically and horizontally over the image to obtain attributes such as borders, areas, forms, etc., from the source image. The model starts to find better features using augmentation, such as blurring, sharpening, texturing, and gradient direction. In the CNN architecture, convolutional layers with a \(2\times 2\) kernel size are Equation (1), which explains the convolutional layers.  
\[C(h,w)=\sum_{x}\sum_{y}i(h-x,w-y)f(x,y) \tag{1}\]  
The dimension of the converted feature map is reduced in the following level, recognized as pooling layers. The CNN model offers a variety of pooling layers, including maximum, minimal, and average pools. To obtain the standout features from the altered feature map. We use max pooling, as shown in Equation (2).  
\[f(z)=\max(0,z) \tag{2}\]  
We have selected the Softmax function to serve as the activation function for the output layer of our proposed model.  
\[\text{Softmax}(z)_{j}=\frac{\exp(z_{j})}{\sum_{i=1}^{n}\exp(x_{i})} \tag{3}\]  
This function predicts a multinomial probability where the odds of each value are inversely correlated with their respective sizes in the vector. Equation (3) defines the outcome range s 0 to 3 for the Softmax activation function [49].  
Figure 2: Basic architecture of CNN.
# Autonomous Driving Decision-Making Method Based on Spatial-Temporal Fusion Trajectory Prediction
## 3 Results and Analysis
### Unprotected Five-way Intersection  
In the five-turn intersection scenario shown in Figure 6c, this paper sets four points that may conflict with the traveling route of the ego-vehicle. Among them, vehicles A, B, and D are traveling straight, which may form a positive conflict with the traveling route of the ego-vehicle; while vehicle C executes a left turn and merges into the same target lane with the ego-vehicle after the turn, and once the ego-vehicle completes the turn, only vehicle C is still in the range of interest of the ego-vehicle.  
Considering the multiple interacting intelligences involved in the scenario; in order to more comprehensively verify the robustness of the algorithms to complex scenarios, this paper gradually increases the number of obstacle vehicles and observes the performance changes of the three algorithms in the test. As the number of obstacle vehicles increases, the target points also increase, which makes the probability of collision of ego-vehicles in avoidance rise. In addition, in complex scenarios, the autonomous vehicle may also experience the \"robot freeze\" phenomenon, i.e., it slackens off at a very low speed in order to avoid a collision, which leads to an increase in the timeout rate.  
Figure 16: Comparison of actual and predicted trajectories of obstacle vehicle B.  
Figure 15: Comparison of actual and predicted trajectories of obstacle vehicle A.  
As can be seen from the test results in Table 9, with the gradual increase of obstacle cars in the scene, the performance of the three algorithms decreases, and the analysis found that the additional vehicles B and D entered the target vehicle's range of interest in turn, during which the state space of the decision-making algorithm changed, and consequently the algorithm underwent adaptive adjustments in generating the decision values, and therefore the algorithm may have some limitations in responding to dynamic scenarios, and may not be able to make an effective response in a timely manner. Specifically, the success rate of this paper's algorithm decreases by 4% and 19% in turn, and the average test reward decreases by 8% and 25.2% in turn, the success rate of the EGCN-SAC algorithm decreases by 10% and 25% in turn and the average test reward decreases by 38.7%, 25.2% in turn, the success rate of the Bi-SAC algorithm decreases by 22% and 21%, and the average test reward decreases by 10.3% and 40.2% in turn, It can be seen that adding obstacle vehicle B is more likely to have an impact on the autoroute than adding obstacle vehicle D. The algorithm in this paper is clearly more robust to the complexity of the scenario.
## 3 Results and Discussion  
The environmental impacts presented for each process are the sum of the four damage categories used within IMPACT 2002+: resources (non-renewable energy and extracted minerals), climate change, ecosystem quality, and human health, expressed as dimensionless unit points (pt.). Of these categories (Figure 3), climate change was the largest contributor to the overall combined environmental impact score for each of the processes, except Direct Heat Drying, for which resource use (primarily natural gas) was the biggest contributor to the overall environmental impact. On the other extreme, the ecosystem quality damage category contributed minimally to the overall environmental impact of all processes. Comparing the environmental impacts results between processes (Figure 3), Composting had the greatest environmental impacts at 71.1 pt., followed by Direct Heat Drying and Lagoon Storage at 50.9 pt. and 39.8 pt., respectively. TPAD had the lowest overall environmental impact score of all of the processes (18.0 pt.), followed by Air Drying (33.5 pt.). Detailed analyses of these scores are provided below.
## 2 Materials and Methods
### Minimum-Redundancy-Maximum-Relevance (mRmR)  
Minimum-redundancy-maximum-relevance (mRmR) [62; 63] function selection works by means of selecting features that have an excessive correlation with the class and low correlation with different features. After CSP, mRmR is adopted in automatic feature selection by substituting the correlation between variables with the statistical dependence, which is defined through mutual information. It is possible to assess non-linear relationships between two random variables by measuring their mutual information with each other. Furthermore, it reveals how much information can be gleaned from a random variable by monitoring another random variable.  
\[I_{(xy)}=\sum_{i,j}\log\frac{p_{(x_{i},y_{j})}}{p_{(x_{i})}p_{(y_{j})}} \tag{7}\]  
The variables \(x\) and \(y\) are feature vectors in Equation (7), \(p(x,y)\) is the joint probability function, and \(p(x)\), \(p(y)\) are the marginal probability functions of \(x\) and \(y\), respectively. Assume that \(S\) is a predefined set of features and \(h\) is a variable belonging to a specific class. The redundancy of \(S\) is quantified as follows:  
\[W_{I}=\frac{1}{\left|S\right|^{2}}\sum_{i,j\in S}I(i,j) \tag{8}\]  
The relevance of \(S\) is determined by the following criteria:  
\[V_{I}=\frac{1}{\left|S\right|}\sum_{i\in S}I(h,j) \tag{9}\]  
Two methods are used to evaluate \(S\):  
\[MID:\ V_{I}-W_{I} \tag{10}\]  
\[MIQ:\ V_{I}/W_{I} \tag{11}\]  
In the vast majority of cases, \(MIQ\) is proven to be superior to \(MID\). Due to the fact that we cannot test all possible subsets of features \(S\) for a given dataset, the mRMR algorithm employs a forward search approach in its implementation.
# Inter-System Differencing between GPS and BDS for Medium-Baseline RTK Positioning
## 3 Stability Analysis of GPS-BDS DISBs  
### Data Collection  
Since the ionosphere-free code and phase DISBs are the combination of those on L1-B1 and L2-B2, we will directly analyze the characteristics of L1-B1 and L2-B2 DISBs. If the L1-B1 and L2-B2 DISBs are stable, the ionosphere-free DISBs will of course also be stable. Similar to the earlier studies, zero and ultra-short baselines are used since the atmospheric effects can be ignored. The baselines with the same or different receiver types were observed on the campus of Curtin University, Australia and the campus of Southeast University, China. Information of these baselines are shown in Table 2. All the stations on the campus of Curtin University used the antennas of the same type: Trimble TRM59800'. The data were collected from DOY 011, 2017 to DOY 016, 2017 with the sampling interval of 30 s. The baseline 'SEU1-SEU2' used antennas with the same type of 'Harson-HXCCGX601A'. The data were collected from UTC 0:00 to 22:00 on DOY 23, 2016 with the sampling interval of 15 s. Of the seven baselines, the first three use the same receiver types, while the other four use different receiver types.
###### Abstract  
Selective laser melting (SLM), used to fabricate metallic objects with high geometrical complexity, is currently of increasing interest to the fields of medicine and dentistry. SLM-fabricated products should have highly smooth surfaces to minimize the use of post-processing procedures such as finishing and polishing. This study investigated the effect of various laser process parameters (laser power, scan rate, and scan-line spacing) on the surface roughness of a Co-Cr dental alloy that was three-dimensionally (3D) constructed via SLM. Initially, a single-line formation test was used to determine the optimal laser power (200 W) and scan rate (128.6 mm/s) that resulted in beads with an optimal profile. During subsequent multi-layer formation tests, the 3D Co-Cr body with the smoothest surface was produced using a scan-line spacing of 100 \(\upmu\)m. The findings of this study show that laser process parameters have crucial effects on the surface quality of SLM-fabricated Co-Cr dental alloys.  
Co-Cr alloy; powder metallurgy; selective laser melting; surface roughness +
Footnote †: journal: Applied Physics Letters
## 2 Materials and Methods
### Data Pre-Processing and Feature Extraction
#### 2.2.4 Temporal Window Selection and Overlapping Approach  
In accordance with the methodology detailed in [24], a temporal window length of 2 s was set after thoroughly evaluating several potential durations. Each temporal window was labeled with the activity being performed during that interval. To ensure sufficient coverage, the temporal windows were overlapped; specifically, each window began in the middle of the previous one, resulting in a 50% overlap between consecutive windows.  
Figure 3: Histograms showing counts of each: (**a**) activity from all five locations and (**b**) body location on the present dataset.
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 4 An Overview of Deep Learning Techniques
### Regions with CNN Features  
Regions with CNN features (RCNN) is an architecture used for segmentation and object detection. RCNN is called region-based because the input is divided into several regions to detect the object of interest, as shown in the Figure 6 [21]. RCNN takes around 45 s for processing per video frame, making it unsuitable for real-time application.
###### Abstract  
Natural zeolites are used as adsorbents in purification processes due to their cation-exchange ability and molecular sieve properties. Surface modified natural zeolites (SMNZs), produced by attaching cationic organic surfactants to the external surface, can simultaneously act as ionic exchangers and organic molecule adsorbents. In this paper, SMNZs were produced and investigated as adsorbents for As(V) removal from wastewater: two natural zeolites, clinoptiolite and phillipsite, were modified using HDTMA-Br and HDTMA-Cl as surfactants. The obtained samples were then characterized under static and dynamic conditions. Results showed that As(V) removal follows a pseudo-second order kinetic, with fast adsorption rates: every sample reached 100 % removal in 2 h, while equilibrium data showed a Langmuir-like behavior, with a greater anion uptake by the HDTMA-Br modified SMNZs due to the formation of a compact and complete micellar structure. Finally, fixed-bed tests were performed to characterize the samples under dynamic conditions assessing the effect of severe operating parameters on the dynamic exchange capacity, selectivity and efficiency of the process. The obtained results demonstrate a good ability of the tested materials to adsorb As(V) from wastewater, confirming the effectiveness of the proposed surface modification technique in expanding the possibility of using natural zeolites in these processes.  
surface modified natural zeolites (SMNZs); HDTMA-Br/Cl; anion exchange; arsenic removal; mathematical modeling +
Footnote †: journal: Applied Soft. 2020, 10, 6939; doi:10.3390/app10196939
## 4 Results and Analysis
### Comparative Studies
#### 4.4.1 Performance of Established CNN Architectures  
To compare the performance of the proposed CNN model, we also employed other CNN architectures: Inception and MobileNet. A brief overview of Inception and MobileNet is described below. These two architectures were selected based on their characteristics by keeping in mind the availability of limited resources.  
**Inception.** This architecture resembles a scanty CNN with a condensed structure. It is a blend of 1 \(\times\) 1, 3 \(\times\) 3, and 5 \(\times\) 5 convolutional layers where their outputs are integrated into a single vector, developing the input for the next level. It allows the internal layers to select the dimension of the filters that are required for learning. Each layer is a continual enhancement of the past layer. The number of the convolutional filters of an individual kernel size is kept low because of the effectiveness of a lesser number of neurons. Another characteristic is having a bottleneck layer, which reduces the computation  
Figure 12: The relative differences in accuracy with increasing number of authors.  
Figure 13: Heat map obtained for datasets of disparate sizes.  
costs. Furthermore, it replaces the fully connected layers with a pooling that moderates the estimates of the filters across the 2D feature map and thoroughly decreases the total number of parameters.  
**MobileNet.** It is devised to productively enhance accuracy while keeping in mind limited resources. It requires less latency and power to encounter the resource limitations of various instances. This model is a heap of independent convolution layers comprising depthwise and pointwise convolutions that separately perform convolution in spatial sizes and input-output channels.  
The experiments were performed on both the English (C50 [42]) and Bangla datasets. The results obtained are given in Table 11. The variation in the accuracy of author identification is also shown through the heat maps on Figure 14a,b. The shown heat maps illustrate the model in which the datasets obtained maximum accuracies (for the English dataset, it was the Inception model, whereas for the Bangla dataset, it was the MobileNet model). It is clear from the heat maps that there was confusion between multiple authors for Inception, which was reduced significantly for MobileNet. Though there was confusion among a lesser number of authors, the percentage of confusion was higher, leading to lower interclass accuracies.
## 4 Applications of InSAR in Landslide Monitoring
### Recent Advances in Landslide Monitoring with InSAR
#### 4.2.5 Advancements in Integrated Approaches Combining InSAR and Other Techniques
Some studies tend to conduct probabilistic analysis instead of using deterministic models [124] as it reduces the uncertainties in soil properties' variations by considering a range of possible outcomes rather than fixed parameters.
# Data-Aided and Non-Data-Aided SNR Estimators for CPM Signals in Ka-Band Satellite Communications
## 4 SNR Estimators
### DA ML Estimator  
The probability density function of the received sequence in Formula (20) is  
\[p(r_{k})=\frac{1}{\sqrt{2\pi}\delta}\exp(-\frac{(r_{k}-As_{k})^{2}}{2\delta^{2 }}). \tag{21}\]  
Given the statistical independence in sampling, Formula (21) can be expressed as  
\[p(\mathbf{r})=(\frac{1}{2\pi\delta^{2}})^{K/2}\exp(-\sum\limits_{k=1}^{K}\frac {(r_{k}-As_{k})^{2}}{2\delta^{2}}), \tag{22}\]  
and the corresponding log-likelihood function is  
\[\ln p(\mathbf{r})=-\frac{K}{2}\ln 2\pi\delta^{2}-\frac{1}{2\delta^{2}}\sum \limits_{k=1}^{K}{(r_{k}-As_{k})^{2}}. \tag{23}\]  
The partial derivative of \(A\) and \(\delta^{2}\) is respectively set as  
\[\frac{\partial\ln p(\mathbf{r})}{\partial A}=\frac{1}{\delta^{2}}\sum \limits_{k=1}^{K}{(r_{k}-As_{k})s_{k}} \tag{24}\]  
and  
\[\frac{\partial\ln p(\mathbf{r})}{\partial\delta^{2}}=\frac{1}{2\delta^{4}} \sum\limits_{k=1}^{K}{(r_{k}-As_{k})^{2}}-\frac{K}{2\delta^{2}}. \tag{25}\]  
where, \(\ln p(\mathbf{r})\) has a maximum value if the result is zero. \(A\) and \(\delta^{2}\) can be expressed, respectively as  
\[\hat{A}_{ML}=\frac{\sum\limits_{k=1}^{K}{r_{k}*s_{k}}}{\sum\limits_{k=1}^{K}{ s_{k}*s_{k}}} \tag{26}\]  
and  
\[\delta^{2}_{ML}=\frac{\sum\limits_{k=1}^{K}{(r_{k}-\hat{A}_{ML}s_{k})}^{2}}{K}. \tag{27}\]  
The SNR estimate is computed using  
\[SNR=10\log_{10}(\frac{\hat{A}_{ML}^{2}}{m_{1}^{2}\delta_{ML}^{2}}). \tag{28}\]
# On the Feasibility of Using Industrial Urea to Mitigate Thermal and Shrinkage Cracking in Concrete
## 4 Results and Discussion
### Dry Shrinkage Strain  
CaCO\({}_{3}\) production as given by Equation (2) restricts crack formation in concrete and is expected to not only make the cement microstructure compact but also offset a certain portion of the dry shrinkage strain. Figure 6 shows the dry shrinkage strain results measured at 30-min intervals using an embedded strain gauge. OPC showed a sharp strain during early aging and a stable tendency after 40-day aging. The highest strain of 3.58 \(\times\) 10\({}^{-6}\) was seen with 56-day aging; this was in the range of the general shrinkage rate of concrete. By contrast, urea mixtures showed a 7.6% (U5), 23.4% (U10), 41.0% (U15V), and 48.7% (U15W) lower shrinkage rate compared to that of OPC. The shrinkage reduction in concrete mixed with urea is considered attributable to CaCO\({}_{3}\) production. This assumption is supported by the fact that the urea flow-out phenomenon was observed during the experiment (Figure 7). Consequently, it is believed that CaCO\({}_{3}\), a reaction product of urea, is closely associated with shrinkage strain.  
Figure 5: Carbonation depths of the concrete mixture.  
Existing low-heat mixtures such as SCM and LHC show a strain similar to that of OPC. In general, the early hydration of LHC is slow, and the strength development takes a long time to end [7]. Therefore, it is more likely to cause dry shrinkage because the remaining water quantity is relatively high without contributing to hydration during early placing [12; 31]. This is one of the reasons why it is difficult to control both dry shrinkage and hydration heat simultaneously. However, high durability is required in dams or marine structures, and concrete mixed with urea could be a promising alternative for these applications.