esa-sceva/satcom-chunk-collection huggingface dataset:
# Temporal Modeling on Multi-Temporal-Scale Spatiotemporal Atoms for Action Recognition
## 3 Related Works
### Knowledge Transfer to Optical Flow  
The optical flow (OF), which carries the motion information of the action, is used as the input of the 2D and 3D CNN architectures for action recognition. In each temporal scale, we also build OF streams to extract deep spatiotemporal information from optical flow. [PERSON] et al. [7] trained a 2D CNN from scratch using stacked optical flow. [PERSON] et al. [11] trained a 3D CNN from scratch using optical flow. Fine-tuning a pre-trained model has been shown to be effective for transferring the knowledge learned from a large-scale dataset to a new dataset or a new modality. [PERSON] et al. [35] fine-tuned a 2D CNN on optical flow using the knowledge learned from large-scale RGB image dataset. In this paper, we fine-tune a 3D CNN on optical flow using the knowledge learned from a RGB video dataset.  
The pre-trained Res3D model provided by [PERSON] et al. [13] takes 3-channel RGB clips as the input of CNN. To transfer the knowledge learned from the RGB to optical flow, we first design 3-channel optical flow. We extract the optical flow for each video action and discretize it by a linear transformation such that the optical flow values fall into the range [0, 255]. This transformation yields 2 channels of optical flow: the horizontal optical flow OF_x and the vertical optical flow OF_y. Then, we calculate the mean of OF_x and OF_y as the third channel. Figure 3 presents two consecutive frames, OF_x, OF_y and the 3-channel optical flow. This step ensures that the optical flow has same number of channels as the RGB used for the input of the Res3D architecture. To transfer the RGB knowledge to the optical flow, which is a new modality, we empirically increase learning rate, step size and the number of iteration during fine-tuning. Compared with RGB Res3D, we increase them in OF Res3D, respectively, by 10 times, 5 times and 5 times to accelerate the model updating. We conducted the evaluation of this fine-tuning and training from scratch in the OF stream of 8f temporal scale on UCF101 split-1. Figure 4 presents the comparison of these two methods by presenting training loss and clip accuracy. With the same number of iterations, the loss of the fine-tuning is smaller and the recognition accuracy using the model learned by fine-tuning is higher. As illustrated in Figure 4, after 20 epochs of iterations, the clip accuracy of OF Res3D learned by fine-tuning achieves 69.9%, while the clip accuracy of OF Res3D learned by training from scratch only achieves 62.2%. All these results show that this transfer learning works well and with the same number of iterations, fine-tuning using the pre-trained model outperforms the training from scratch.  
Figure 4: Comparison of two types of training: (**a**) fine-tuning using the pre-trained model; and (**b**) training from scratch. The experiment is conducted in in the optical flow (OF) stream of 8f temporal scale on UCF101 split-1.  
Figure 3: Two consecutive frames, optical flow in the horizontal and vertical directions and 3-channel optical flow.
# Optimal Path Routing Protocol for Warning Messages Dissemination for Highway VANET
## 5 Performance Evaluation
### Throughput  
This parameter, which can be calculated, refers to the proportion of packets received on the destination nodes of all packages sent by the source nodes.  
\[T_{r}=\frac{\Sigma_{i=1}^{R_{p}}R_{p_{i}}}{P_{t}}, \tag{11}\]  
where \(T_{r}\) refers to the network throughput gain, \(P_{t}\) refers to the total number of packets sent by the source node, and \(R_{p_{i}}\) denotes a specific packet received by a target node.  
As it counts the number of packets successfully delivered to target nodes, throughput is a crucial metric for performance evaluation. End-to-end delays and packet losses impact the throughput of the network. Figure 7a,b show improved throughput of OPRP compared to DABFS and ID-LAR due to our original direction-based path selection. Additionally, OPRP follows the same pattern under challenging circumstances where the source and destination are on the opposite sides of the road by significantly improving throughput, as demonstrated in Figure 7c,d. Additionally, as shown in Figure 7e, OPRP outperformed the other protocols, improving throughput restoration state packet forwarding.  
Moreover, the packet loss ratio and end-to-end delay decrease as the density increases because nodes are closer. As a result, the throughput increases. Therefore, the proposed protocol with clustering and the compared protocols without clustering are clearly illustrated in the results.
# Improved Localization and Recognition of Handwritten Digits on MNIST Dataset with ConvGRU
## 4 Experiments
### MNIST Dataset  
The MNIST dataset [40] is an ideal choice for conducting handwritten digit recognition experiments. It consists of 70,000 samples written by 250 different individuals. Each sample is a \(28\times 28\) pixel image of a handwritten digit, recording the grayscale value of each pixel. The MNIST dataset has become the standard benchmark for testing the accuracy of handwritten digit classifiers. Additionally, there is a handwritten digit recognition competition based on the MNIST dataset on the Kaggle platform.
# RoPT: Route-Planning Model with Transformer
###### Abstract  
With the increasing aggravations of urban traffic jams, intelligent route planning to reduce traffic time is becoming increasingly critical for drivers. However, traditional route-planning methods, such as graph search and Recurrent Neural Network (RNN)-based methods, struggle to capture the complex dynamics of road networks. Specifically in A*-type methods, routes should be searched instantly on the whole graph for the sake of dynamic changes in edge time consumption. As for RNN-based methods, their shortcomings in capturing long-distance sequence dependencies makes them unsuitable for route planning in metropolises with long routes. Therefore, to better adapt to the complexity of urban traffic, in this paper, an innovative route-planning model called Route Planning with Transformer (RoPT) is proposed. This model is based on the fusion of Graph Convolutional Networks (GCNs) and a Transformer, which uses GCNs for capturing complex spatial dependencies between the current intersection and the destination in a road network. Depending on the self-attention mechanism of the Transformer, the long-distance temporal dependencies between intersections could also be captured effectively. With comprehensive experiments on two real-world traffic datasets, the Porto dataset and the Chengdu dataset, it is demonstrated that RoPT outperforms the best methods, to the best of our knowledge. Moreover, the latent features learned from RoPT are more interpretable.  
Keywords:route planning; transformer; traffic; spatial-temporal data; GCN; attention mechanism +
Footnote †: journal: Applied sciences
# Integration of a Mobile Node into a Hybrid Wireless Sensor Network for Urban Environments +
## 4 Experiments  
A series of experiments has been designed to validate the concept of mobile node. This validation includes using different combinations of nodes and sensors. The experiments have been performed for the two working modes: local and networked. The mobile node was installed in the modified top carrier of a test vehicle. The selected test vehicle was an electric passenger car (Nissan Leaf, Yokohama, Japan) to prevent perturbation of gas and noise measurements (see Figure 11). The experiments consisted in a series of routes along the city of Malaga, in Spain. The Receiver node within the mobile node synchronized its internal database with the database in an external server. A graphical  
Figure 10: Human–Machine Interface for the Urban Information System.  
Human-Machine Interface (HMI), running on a laptop, allowed for monitoring in real-time the gathered data related to their geographic position.
###### Abstract  
Fermentation parameters for surfactin production and sporulation in a submerged culture of _Bacillus subtilis_ Y9 with various oxygen transfer rates in 5 L jar fermenters were investigated. The oxygen-uptake rate (OUR) was positively correlated with volumetric surfactin productivity. When OUR value increased from 0 to 250 s\({}^{-1}\), productivity increased up to 45 mg/L-h; however, no further increase was observed at OUR values above 255 s\({}^{-1}\). The volumetric mass transfer coefficient \(K_{L}a\) increased with increasing agitation speed. However, a reduction in surfactin production was observed at the highest agitation speed of 500 rpm. Productivity sharply decreased after spore appearance, and remained low until the end of the culture. A mesh-type sparger was installed to generate microsized air bubbles. When the system was operated at 400 rpm with the mesh-type sparger, \(K_{L}a\) was higher than that at 500 rpm with an original sparger. Under agitation at 400 rpm with the mesh-type sparger, productivity was maintained above 42.3 mg/L-h until 24 h, resulting in the highest surfactin concentration of 875 mg/L. Thus, a mesh-type sparger promotes \(K_{L}a\), leading to an increase in productivity.  
Basillus subtilis Y9; mesh-type sparger; oxygen-uptake rate; surfactin; volumetric-mass transfer rate +
Footnote †: journal: Applied Soft
## 1 Introduction  
Fracture toughness is defined as material resistance against crack growth [1]. The development and coalescence of cracks are the most important factors of rock failure with quasi-brittle behavior. Therefore, for analyzing the failure of rock materials, determining the fracture toughness is essential. This parameter is widely used in the fields of rock blasting, underground space stability, hydraulic fracturing, earthquake dynamic analysis, rock slope stability, and for evaluation of the drill-ability of rock masses.  
Different studies have recently been conducted to develop some models for predicting the mechanical properties [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14] and fracture toughness [15, 16, 17, 18, 19, 20] of rock materials, including limestone. [PERSON] et al. [21] reported a reverse relationship between mechanical properties and the percentages of sparite and allocations, and a direct relationship between the mechanical properties and the percentages of micrite and dolomite of limestone. [PERSON] et al. [22] provided some relationship between the mechanical properties with the basic physical and dynamic characteristics of igneous rocks. [PERSON] et al. [23] showed that the strength of sedimentary rocks decreased when the water content increased; consequently, the mechanical properties are strongly influenced by the percentage of water saturation. In addition, they estimated the rock fracture toughness using Young's modulus and the Brazilian tensile strength. By investigating the effect of limestone texture on its mechanical behavior,Ajalloian et al. [24] concluded that the best model for predicting Young's modulus of carbonate rocks is multiple regression, by using some petrographic characteristics such as dolomitic percentage, the average grain size, and the percentage of allocations and carbonate. [PERSON] et al. [25] reported a close relationship between \(\mathrm{K_{IC}}\) and the physicochemical properties of rock materials. According to [PERSON] et al. [26], \(\mathrm{K_{IC}}\) can be predicted using brittleness index. Some empirical relationships to predict \(\mathrm{K_{IC}}\) from the index properties are outlined in Table 1.  
In this study, the effect of the saturation condition on the fracture toughness of limestone has been investigated, considering the types of petrography. Moreover, because of the complexity of measuring fracture toughness, the index characteristics of the limestones studied were determined, and their correlations with fracture toughness were examined. As the determination of \(\mathrm{K_{IC}}\) is one of the most destructive, time consuming, and costly tests, it is very useful to predict it using non-destructive and simple test methods, which is one of the main aims of this study.
## 3 Experimental Results
### Heat of Hydration
In the binary system of cement and quartz powder, hydration heat is released by the chemical reaction of the cement. We can thus normalize the hydration heat measurements to the mass of the cement. The results are shown in Figure 3c. After normalization by the cement mass, the heat release value of the 7.5% quartz powder is similar to that of the standard specimen. The nucleation effect of the 15% quartz powder is more obvious, and the hydration heat is significantly higher than that of the standard specimen [35; 36]. In cement-quartz binary blends, quartz is a chemically inert filler, and all hydration comes from cement hydration. The results for the cement's cumulative hydration heat per gram are shown in Figure 3d. Overall, as the amount of substitution increases, the heat release per gram of cement increases. This can mainly be attributed to the nucleating effect of the quartz powder.
## 5 Simulation Results and Discussion
### EE Performance
In Figure 11, \(K=20\) and we consider the change of the EE with the number of receiver antennas. It is seen that the MMB-LR-SIC with \(L=4\) has the lowest EE in the case of a large number of antennas, due to the high power consumed by signal processing, while in relatively small cells the transmission power is low for all receivers. The nonlinear receivers are advantageous when the number of BS antennas is smaller (When the number of BS antennas is small, the channel becomes more ill-conditioned. Consequently, the linear detectors based on the (pseudo)-inverse or regularized inverse of the channel matrix can introduce significant amplification of the noise. Nonlinear receivers, after interference cancellation, work on a better-conditioned channel, which results in less significant noise enhancement). In Figure 12, we can see that as computational efficiency increases, nonlinear receivers become advantageous in a larger range of cases. Figure 11: Energy efficiency for \(K=20\) UEs and 6-bit ADCs at the BS in \(d_{\max}=400\) m at \(\text{BER}_{0}=10^{-2}\), \(L_{\text{BS}}=75\) GFlops. Figure 12: Energy efficiency for \(K=20\) UEs and \(b=6\)-bit ADCs at the BS with \(d_{\max}=400\) m at \(\text{BER}_{0}=10^{-2}\), and \(L_{\text{BS}}=500\) GFlops/W.
# Analytical Solutions for the Propagation of UltraShort and UltraSharp Pulses in Dispersive Media
## 4 Gaussian Pulse  
The simplest pulse, which is ubiquitous in the literature, is the Gaussian one. This is one of the few cases where the pulse has the same shape as its Fourier Transform. Therefore, since the dispersion dynamics can be formulated as a convolution with a complex Gaussian (Equation (4)), then in general, the pulse profile remains a complex Gaussian. In this case, the initial pulse profile is  
\[A(t,0)=A_{0}\exp\Bigl{(}-(t/\theta)^{2}\Bigr{)} \tag{13}\]  
and thus the pulse's profile at the end of the dispersive medium is (following Equation (12))  
\[A(t,z)=\frac{A_{0}\theta}{\sqrt{\theta^{2}-2i\beta_{2}z}}\exp\biggl{(}-\frac{1 }{\theta^{2}-2i\beta_{2}z}t^{2}\biggr{)} \tag{14}\]  
The profile of Equation (14) is plotted in Figure 1 and a spatiotemporal presentation of its intensity \(\left|A(t,z)\right|^{2}\) is presented in Figure 2. Hereinafter, we adopt the following dimensionless parameters: \(\tau\equiv t/\theta\) and \(\zeta\equiv\beta_{2}z/\theta^{2}\).  
Figure 1: The real (upper panel) and imaginary (lower panel) components of the pulse (Equation (14)). The dashed curve represents the initial profile (for \(\zeta=\beta_{2}z/\theta^{2}=0\)), while the solid curve represents the signal after a distance, which corresponds to \(\zeta=\beta_{2}z/\theta^{2}=2\). Time is measured in units of \(\theta\), which corresponds to the pulse’s temporal width.  
Sample Application: The pulses that many mode locked lasers emit can be simulated by Gaussian pulses (see Ref. [14]). When such a pulse with a Full Width at High Maximum (FWHM) of 1 ps (which corresponds to \(\theta=FWHM/\sqrt{\ln 4}\cong 0.85\) ps) and a carrier wavelength of \(\lambda=1.06\)\(\upmu\)m enters a BK7 glass, then \(|\beta_{2}|\cong 24\) ps\({}^{2}\)/Km, and \(\zeta=1\) in Figure 2 corresponds to \(z=\zeta\theta^{2}/\beta_{2}\cong 30\) m.
## 2 System Model and Problem Statement
We can now succinctly state the problem to be solved as follows: _for a set of service requests \(\mathcal{Q}\) generated by a set of UEs \(\mathcal{J}\), we seek to jointly determine the location of the VNFs that must be deployed to **properly serve** the requested SFCs, as well as the E2E routing path for each request, so that the total system power consumption is minimized (equivalently, the energy efficiency in bits/foule over all requests is maximized)_. Computation of the selected routing path also includes determination of the BS to which each UE attaches to.
# Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations
## 5 Determination of \(\Gamma\)
As \(\left(\frac{\sqrt{\lambda_{\mathbf{\Theta}}^{\max}}}{\lambda_{\mathbf{\Theta}}^{\min} }\right)_{\text{lookup}}>\text{calculated }\left(\frac{\sqrt{\lambda_{\mathbf{\Theta}}^{\max}}}{\lambda_{\mathbf{\Theta}}^{\min} }\right)\), \(\Gamma^{\prime}\) is greater than the actual \(\Gamma^{\prime}\). Hence, the \(\Gamma\) from Equation (39) is more than the actual \(\Gamma\). The \(\Gamma\) corresponding to \(\alpha_{2}\) is also obtained in the same way. Performance and salient features of the modified KF RAIM are studied next for simulated scenarios of aircraft flight.
# Feature Selection and Transfer Learning for Alzheimer's Disease Clinical Diagnosis
###### Abstract  
Background and Purpose:A majority studies on diagnosis of Alzheimer's Disease (AD) are based on an assumption: the training and testing data are drawn from the same distribution. However, in the diagnosis of AD and mild cognitive impairment (MCI), this identical-distribution assumption may not hold. To solve this problem, we utilize the transfer learning method into the diagnosis of AD. **Methods:** The MR (Magnetic Resonance) images were segmented using spm-Dartel toolbox and registrated with Automatic Anatomical Labeling (AAL) atlas, then the gray matter (GM) tissue volume of the anatomical region were computed as characteristic parameter. The information gain was introduced for feature selection. The TrAdaboost algorithm was used to classify AD, MCI, and normal controls (NC) data from Alzheimer's Disease Neuroimaging Initiative (ADNI) database, meanwhile, the \"knowledge\" learned from ADNI was transferred to AD samples from local hospital. The classification accuracy, sensitivity and specificity were calculated and compared with four classical algorithms. **Results:** In the experiment of transfer task: AD to MCI, 177 AD and 40 NC subjects were grouped as training data; 245 MCI and 45 remaining NC subjects were combined as testing data, the highest accuracy achieved 85.4%, higher than the other four classical algorithms. Meanwhile, feature selection that is based on information gain reduced the features from 90 to 7, controlled the redundancy efficiently. In the experiment of transfer task: ADNI to local hospital data, the highest accuracy achieved 93.7%, and the specificity achieved 100%. **Conclusions:** The experimental results showed that our algorithm has a clear advantage over classic classification methods with higher accuracy and less fluctuation.  
Keywords:transfer Learning; feature selection; Alzheimer's Disease; mild cognitive impairment; MR image processing +
Footnote †: journal: Medical Imaging
# Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation +
## 4 Discussion  
The results shown above are location specific. Different performance could be expected with a change in location, but the MS positions considered in this study are in the order of thousands, so it could be expected to produce similar results providing the characteristics of the environment was similar. To that end, an experiment was also conducted using data from a separate area of the city, for the NLOS environment, and a different open area, for the LOS environment, and similar CDF curves were observed.
## 4 Engineering Test Verification
### Comparative Study of Diagnostic Methods  
In order to further verify the superiority of the proposed method, it is compared with the commonly used signal decomposition method and the commonly used information entropy method. Using EEMD-MDE, EEMD-HFE, EEMD-HDE, VMD-HFE, VMD-MDE, and VMD-HDE methods to extract the fuel jet pressure wave signal fault characteristics, taking the normal operating condition fuel pressure wave IMF1 component as an example, the extracted information entropy characteristics are shown in Figure 15. As shown in Figure 15 and Table 5, the CV value and calculation time of the VMD-HDE are the smallest, which indicates that the method has the best performance in evaluating the time series complexity, and it has high calculation accuracy, good stability, and the highest computational efficiency.  
Then, the decomposed IMF component is classified and identified as the feature vector input SVM. The 10 sets of feature vectors of the three states are input into the SVM classifier as the training samples, and the 20 sets of feature vectors are used as the test samples of the input SVM classifier. The classification results are shown in Table 6. Taking EEMD-HFE and VMD-HFE, EEMD-MDE and VMD-MDE, EEMD-HDE and VMD-HDE as examples, the classification accuracy of VMD is higher than EEMD, and the calculation time is short. The computational efficiency is high. Taking VMD-HFE, VMD-MDE, and VMD-HDE as examples, HDE performs better on time series complexity metrics and has the highest computational efficiency. Therefore, the effectiveness and superiority of the proposed high-voltage common rail injector fault diagnosis method based on IFOA-VMD and hierarchical dispersion entropy are verified.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **EEMD-HFE** & **EEMD-MDE** & **EEMD-HDE** & **VMD-HFE** & **VMD-MDE** & **VMD-HDE** \\ \hline Classification & & & & & & \\ accuracy/\% & 88.8 & 92.2 & 93.3 & 95.5 & 97.7 & 100 \\ Time/s & 20.8 & 11.9 & 11.4 & 13.7 & 4.7 & 4.2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of classification results of feature extraction methods.  
Figure 15: Information entropy characteristics of the IMF1 component.
# Face Spoofing Detection with Stacking Ensembles in Work Time Registration System
## Appendix A
\begin{table}
\begin{tabular}{c c c c c} \hline
**Threshold** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline
0.05 & 97.23 \(\pm\) 1.77 & 96.56 \(\pm\) 3.97 & 93.40 \(\pm\) 7.42 & 94.67 \(\pm\) 3.38 \\ \hline
0.10 & 97.23 \(\pm\) 1.77 & 95.64 \(\pm\) 4.12 & 95.49 \(\pm\) 6.75 & 95.32 \(\pm\) 3.20 \\ \hline
0.15 & 97.60 \(\pm\) 1.32 & 95.43 \(\pm\) 3.92 & 96.46 \(\pm\) 6.10 & 95.74 \(\pm\) 3.01 \\ \hline
0.20 & 97.96 \(\pm\) 1.12 & 95.52 \(\pm\) 3.71 & 96.99 \(\pm\) 5.58 & 96.08 \(\pm\) 2.86 \\ \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Threshold** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline
0.05 & 98.54 \(\pm\) 0.10 & 98.27 \(\pm\) 2.16 & 98.27 \(\pm\) 2.76 & 98.24 \(\pm\) 1.79 \\ \hline
0.10 & 98.78 \(\pm\) 0.15 & 98.25 \(\pm\) 2.14 & 98.31 \(\pm\) 2.74 & 98.25 \(\pm\) 1.77 \\ \hline
0.15 & 98.96 \(\pm\) 0.12 & 98.24 \(\pm\) 2.12 & 98.34 \(\pm\) 2.71 & 98.26 \(\pm\) 1.75 \\ \hline
0.20 & 99.03 \(\pm\) 0.13 & 98.24 \(\pm\) 2.09 & 98.37 \(\pm\) 2.69 & 98.27 \(\pm\) 1.73 \\ \hline
0.25 & 99.08 \(\pm\) 0.12 & 98.24 \(\pm\) 2.07 & 98.40 \(\pm\) 2.66 & 98.29 \(\pm\) 1.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Obtained metrics [%] in a 5-fold cross-validation for different values of threshold of the stacking ensemble B,S,C,N,X,E,V. The threshold is the probability above which images are considered spoofed. CelebA dataset.