esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### O\({}_{2}\) Broadening Coefficient  
The HCl-O\({}_{2}\) broadening measurement procedure was the same as for the HCl-Ar broadening experiment, the buffer gas used here was pure O\({}_{2}\). As an example, Figure 5a shows a measured absorption spectrum of HCl in O\({}_{2}\) at the total pressure of 101.3 mbar. The measured spectrum was averaged for 10 scans and fitted by the Voigt profile. Typical average residuals (optical density) were around (1\(\sigma\)) 5 \(\times\) 10\({}^{-4}\), which corresponds to a SNR = 110 for the spectrum of 13.4 \(\upmu\)mol/mol HCl. The measured HCl-O\({}_{2}\) broadening coefficient is \(\gamma_{\rm O2}\) = 34.50(73) \(\times\) 10\({}^{-3}\) cm\({}^{-1}\)/atm at 294 K according to the slope of the linear regression of the Lorentzian widths versus pressures, with an intercept of (6.58 \(\pm\) 5.34) \(\times\) 10\({}^{-4}\) cm\({}^{-1}\).  
Figure 4: (**a**) Measured Ar-broadened HCl absorption spectra at 160.5 mbar and the fitting residual; (**b**) Measured Ar induced pressure broadening with a linear fitting model. The residual between fit and data is shown below.
## 2 Intelligent Processing on Image and Optical Information
### Estimation and Localization  
For autonomous driving, it is important to obtain precise and high-frequency localization of the vehicle. The inertial measurement unit (IMU), wheel encoder, and lidar odometry are utilized together to estimate the ego-motion of the unmanned ground vehicle in the paper entitled 'IMU-Aided High-Frequency Lidar Odometry for Autonomous Driving' by [PERSON], [PERSON], and [PERSON] [13].  
Dough fermentation plays an essential role in the bread production process, and its success is critical to producing high-quality products. In the paper entitled 'Determination of the Optimal State of Dough Fermentation in Bread Production by Using Optical Sensors and Deep Learning', [PERSON], [PERSON], [PERSON], and [PERSON] [14] proposed a novel method for the continuous monitoring of the volumetric parameters of dough piece during the fermentation process.
## 3 Results and Discussion
### Numerical Results  
As it has been reported in Section 2.2, the numerical analysis was performed iteratively to reproduce the experimental results. As it is explained in literature about the selected numerical model, all the characterizing parameters interact with each other to give the overall behavior. It follows that the selection of the best parameters of the numerical model is a complex task and, in the present paper, it was performed based on the following criteria: (a) the elastic phase must be reproduced correctly (maximum absolute error equal to 2%); (b) the strength of all the tests must be reproduced with a maximum absolute error equal to 10%; (c) the area under the compression and bending tests must be reproduced with a maximum absolute error equal to 10% and 20%, respectively.  
Figure 9: Fracture energy vs. fiber content (black line: compression test; red line: splitting test; blue line: bending test); (a) \(G_{f}\); (b) \(G_{f}/G_{f,0}\).  
The optimal material parameters obtained as described before are reported in Table 1 and graphically sketched in Figure 10.  
Figure 10: Microplane parameters vs. fiber content: (**a**) \(\gamma_{c0}\); (**b**) \(\beta_{c}\) (blue line) and \(\beta_{t}\) (red line); (**c**) \(D\).
## Methodology
### The Principle of the SPSO-DE Hybrid Algorithm  
In this study, to avoid the swarm diversity declining rapidly and the local optima, the Differential Evolution (DE) algorithm is proposed to modify the personal best food source positions. The detail of the DE is available in Reference [21; 22; 23]. During the DE algorithm, the mutant vector can be derived as [23]:  
\[\mathbf{P}_{i,\mathrm{mut}}\left(t\right)=\mathbf{P}_{k}(t)+F\cdot\left[ \mathbf{P}_{j}(t)-\mathbf{P}_{m}(t)\right] \tag{5}\]  
where \(k\), \(l\), \(m\) are random integers uniformly selected from \([1,\,N_{\mathrm{s}}]\), which are different from each other; \(F\in[0,\,2]\), a real and constant value, denotes the mutant factor. After the crossover process, the trial vector \(\mathbf{P}_{i,\mathrm{tri}}(t)=(p_{i1,\mathrm{tri}},\,p_{i2,\mathrm{tri}},\,p_{ i3,\mathrm{tri}}\ldots,\,p_{iN_{\mathrm{p}},\mathrm{tri}})\) can be expressed as [23]:  
\[p_{ij,\mathrm{tri}}\left(t\right) = \left\{\begin{array}{ll}p_{ij,\mathrm{mut}}(t)&\mathrm{if}\,rand _{3}(j)\leq C_{R}\,\mathrm{or}\,j=rbr(i)\\ p_{ij}(t)&\mathrm{if}\,rand_{3}(j)>C_{R}\,\mathrm{or}\,j\
eq rbr(i)\end{array}\right. \tag{6}\]  
where \(rand_{3}\), a uniform random number, is selected within \([0,\,1]\); \(C_{R}\in[0,\,1]\) is the user-defined crossover constant; \(rbr\in[1,\,N_{\mathrm{s}}]\) is a random integer. The detailed search procedures of the SPSO-DE hybrid algorithm are shown below.  
1. Initialize the system control variables of SPSO-DE hybrid algorithm, such as \(N_{\mathrm{s}}\), \(N_{\mathrm{c}}\), \(N_{\mathrm{p}}\), \(\varepsilon\), \(C_{1}\), \(C_{2}\), \(F\) and \(C_{R}\).
2. Obtain the initial particles' position and velocity for all the swarm randomly; calculate and evaluate the corresponding objective function value \(F_{\mathrm{obj},i}\) of each particle; record the initial personal best position \(\mathbf{P}_{i}\) and objective function value \(pbest_{i}\) of each particle; record the global best position \(\mathbf{P}_{\mathrm{g}}\) and objective function value \(gbest\) of the swarm.
3. Finish the iteration, if the calculation and iteration satisfy the stop criteria: 1. \(gbest\) is less than the tolerance \(\varepsilon\); 2. iteration number is not less than the user-defined iteration limit \(N_{\mathrm{c}}\).
4. Update and obtain the new particle velocity and position according to Equations (3) and (4).
5. Calculate new \(F_{\mathrm{obj},i}\) and evaluate the new positions for each particle.
6. Compare the old \(F_{\mathrm{obj},i}\) with the new one for each particle. Update the corresponding particle information if the new \(F_{\mathrm{obj},i}\) is superior
7. Use the DE algorithm to modify the personal best food source positions according to Equations (5) and (6).
8. Determine if the current personal best position and global best position are superior than the old ones, if so, update them. Loop to Step 3.
## 4 Secure Path Algorithm  
In this paper, we consider a relatively simple WSN model. Each sensor node in the WSN is battery-powered and has limited sensing, computation and wireless communication capabilities. The sink is a data collection center equipped with sufficient computation and storage capabilities. Sensor nodes generate sensed information and aggregate data packets. The sink collects data from sensor nodes periodically. The routing layer of WSNs is threatened by various attacks. However, due to the focus of this paper, it will not be further discussed and here we consider only attacks of packet dropping like selective forwarding and black hole. We assume that compromised nodes, in order to allay suspicions, selectively drop only a small proportion of all packets passing by rather than every packet. If a compromised node drops every packet, it is a black hole, which can also be handled by our approach.  
The process of data collection in WSN is a relay of data packets from the source node to the sink. The approach in this manuscript is mainly based on an assumption that **if a data packet from the source successfully arrives at the sink, the path from the source to the sink is more likely to be safe for subsequent data collection**. Therefore, we can make use of such historical information about data collection to improve the quality of the data collection and even perform intrusion detection.
# Driver Drowsiness Detection by Applying Deep Learning Techniques to Sequences of Images
## 5 Discussion
### Future Works and Improvements  
Since Alternative I uses a neural network that learns the features of the videos by itself, to improve it, we could modify the following:  
* **Training settings**. For example, changing the number of epochs or the learning rate.
* **Model used for transfer learning**. We used EfficientNetB0, which is a model trained for object recognition over the ImageNet database. A domain that could be more similar to the drowsiness detection task could be facial recognition (since in this domain they analyze faces, too), so we could try to use models, such as VGGFace [45].
* **Network's architecture**, especially the section related to the recurrent neural network. GRU is known for being useful for its long-term memory, which helps working with long sequences of data, but the system could benefit from using an architecture specialized in working with massive sequences of data, such as WaveNet [46].  
For both alternatives, it could also be useful to add a third class to the classes \"awake\" and \"drowsy\". This new class, which could be named \"questionable\" as in [12], could be used to avoid false positives when the model is not 100% sure that the driver is drowsy. To do this, the UTA-RLDD database could be used, since it provides an extra class \"low vigilant\" where the subjects are not neither completely alert nor completely drowsy.  
Another possible improvement to the system could be to review the fuzzy inference system, adding new inputs and rules. For example, the CNN in charge of yawning detection could be modified to also identify when the driver is talking, since the drowsiness level of a person that is part of a conversation is usually low.  
Finally, to guarantee the effectiveness of the system in a real driving environment, it would be necessary to test it over data where the subjects are actually driving and the driver state is quantifiably binary (awake/drowsy). The UTA-RLDD dataset is extremely useful because it is realistic and shows videos recorded from a similar angle to that which could be used in vehicles, but the subjects are not driving and their symptoms could vary. In this dataset, the subjects were asked to perform one of these three actions: reading, watching  
Figure 14: (**left**) Frame of subject 60’s drowsy video. (**right**) Landmark detection of the frame.  
Figure 13: (**left**) Frame of subject 60’s awake video. (**right**) Landmark detection of the frame.  
something on their computer, or being idle. While reading, for example, the blinking rate is reduced [47], so the activity performed is relevant when testing the final system.
# Groundwater Prediction Using Machine-Learning Tools
## 4 Groundwater Prediction Methodology
### Feature Selection
#### 4.3.1 Same-Pixel Features  
Our first set of candidate features for prediction of an image pixel consists of the same pixel location for the 12 previous months. A similar choice has been made in previous studies [39; 40; 61]. To select the most important of these 12 features, a sliding window technique is used, as shown  
Figure 3: Overview of dataset preparation for feature selection: (**left**) Example of groundwater image of southern Africa before pre-processing (note image is inverted vertically); (**right**) Notation for same-pixel features used in image prediction.  
in Figure 3 (right): the same pixels within a prior 12-month window were used to predict the corresponding pixel in the 13 th month.  
To evaluate the relative importance of these features, XGB with the gain metric was applied to the training set. Figure 4 shows the results. In the figure, f(0) stands for the same month in the previous year while f(11) stands for the previous month. The graph shows that f(0), f(11), and f(1) (12, 1, and 11 months previous, respectively) have the greatest importance. This finding agrees with [62; 63], which also used the previous and 12-month prior pixels to predict corresponding points in current month.  
Based on our results, we created seven different feature sets labelled **a-g** as follows:  
* **a** = f(0, 11)
* **b** = f(0, 11, 1)
* **c** = f(0, 11, 1, 10)
* **d** = f(0, 11, 1, 10, 2)
* **e** = f(0, 11, 1, 10, 2, 9)
* **f** = f(0, 11, 1, 10, 2, 9, 3)
* **g** = f(0, 11, 1, 10, 2, 9, 3, 8, 4)
# Fast Flow Reconstruction via Robust Invertible \(n\times n\) Convolution
## 4 Invertible \(n\times n\) Convolution
### Invertible Shift Function  
The shift function \(\mathcal{S}\) will approximate all shifted input \(\mathbf{X}^{k}_{c,i,j}\) (\(1\leq k\leq K\)). Here, we propose to design \(\mathcal{S}\) as a linear transformation per channel; specifically, we have learnable variables \(\alpha_{c},\beta_{c};1\leq c\leq C\) are scale and translation parameters for each channel, respectively. The shift function \(\mathcal{S}\) can be formulated as follows:  
\[\mathcal{S}(\mathbf{X}_{c,i,j})=\alpha_{c}\mathbf{X}_{c,i,j}+\beta_{c}, \tag{7}\]  
where \(c,i,j\) are the depth channel index and spatial indices, respectively. The reverse function of \(\mathcal{S}\) can be easy to obtain:  
\[\mathbf{X}_{c,i,j}=\frac{\mathcal{S}(\mathbf{X}_{c,i,j})-\beta_{c}}{\alpha_{c }}. \tag{8}\]  
Thanks to Equation (7), the value of \(\mathcal{S}(\mathbf{X}_{c,i,j})\) only depends on \(\mathbf{X}_{c,i,j}\) and the Jacobian matrix will be in the form of the diagonal matrix as follows:  
\[\begin{split}\mathbf{J}=\frac{\partial\mathcal{S}(\mathbf{X})}{ \partial\mathbf{X}}&=\begin{bmatrix}\frac{\partial\mathcal{S}( \mathbf{X}_{1,1,1})}{\partial\mathbf{X}_{1,1,1}}&0&\cdots&0\\ 0&\frac{\partial\mathcal{S}(\mathbf{X}_{1,1,2})}{\partial\mathbf{X}_{1,1,2}}& \cdots&0\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&\cdots&\frac{\partial\mathcal{S}(\mathbf{X}_{c,i,j,W})}{\partial\mathbf{ X}_{c,i,j,W}}\end{bmatrix}\\ &=\begin{bmatrix}\alpha_{1}&0&\cdots&0\\ 0&\alpha_{1}&\cdots&0\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&\cdots&\alpha_{c}.\end{bmatrix}\end{split} \tag{9}\]  
Therefore, the determinant of Equation (9) is the product of all elements in the diagonal of the matrix \(\mathbf{J}\) as in Equation (10).  
\[\begin{split}\det\left(\frac{\partial\mathcal{S}(\mathbf{X})}{ \partial\mathbf{X}}\right)&=\prod_{c=1}^{C}\alpha_{c}^{H\times W}\\ \log\bigg{|}\det\left(\frac{\partial\mathcal{S}(\mathbf{X})}{ \partial\mathbf{X}}\right)\bigg{|}&=H\times W\times\sum_{c=1}^{C}\log| \alpha_{c}|.\end{split} \tag{10}\]
## 3 Analysis of Results
### Discrete Element Simulation Analysis
#### 3.1.2 Reunion Model Reliability Validation Test  
Before the formal testing begins, a preliminary test is required to verify the reliability of the aggregate model. The pre-test consists of two parts: a discrete element simulation test and a solid model verification test. The reliability of the discrete element model was evaluated, according to the fitting analysis of the simulation test values and the measured values on the test bench.  
Figure 14: Test bench design and physical drawing.  
The parameters of the simulation test are set as follows: the motion mode of the shovel blade is set to Linear Translation, the forward positive direction is the X-axis negative direction, the shovel body material is 65 Mn, the digging depth is 100 mm, the shovel face inclination angle is 25\({}^{\circ}\), the operating speed is 0.3 m/s, the time step is 20%, and the total simulation time is 5 s, with an effective operating time of 3.1 s.  
In the solid model validation test, each parameter of the first-order excavation shovel is kept consistent with the simulation test parameters. After the validation test of the physical model is completed, the measured data and simulation data were fitted and processed to generate the model reliability analysis curve as shown in Figure 15.  
By comparing and analyzing the two curves in Figure 15, it can be seen that the measured value on the test bench is slightly larger than the simulation test value, and the relative error between the simulated test value and the measured value on the test bench is about 14%, which is mainly related to the boundary condition setting of the simulation test and the complexity of soil particle composition. Although the overall simulated test value is smaller than the actual measured value in the test bench, the curve of the simulated test value and the actual measured value in the test bench have the same variation trend within the error tolerance, which indicates that the above discrete element simulation model is reliable and can be used as the technical support for the first-order shovel excavation test.
# Deep Feature Retention Module Network for Texture Classification
## 4 Experiment
### Ablation Study  
We conducted an ablation study to verify the effectiveness of FRM. We compared the performance of DenseNet161, which was used as the backbone, and the model presented in this study to confirm its effectiveness. Table 2 lists the experimental results for the texture datasets. Block 14 is shown in Figure 6a. Its structural characteristic is the form that FRM combines with each DenseBlock. It has all the feature information from low to high level. Comparing the accuracy results to the backbone, Block 14 showed +3.96%, 3.09%, and +11.08% accuracy for DTD, FMD, and KTH-TIPS2b, respectively. The results proved that the combination of the FRM with the backbone model is effective for classification performance. FRM consists of a combination of local parts using convolutions of different sizes and global parts using average values, which can help the model determine the properties of the unique patterns of images in the dataset. That is, it was confirmed that FRM is useful in improving performance for datasets showing regularity and irregularity characteristics. In particular, it showed that it is effective at finding characteristics in images for KTH-TIP2b that have regularity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **DTD** & **FMD** & **KTH-TIP2b** \\ \hline DenseNet161 (Backbone) & 70.57 & 79.67 & 81.89 \\ \hline Block 14 (Ours) & 74.53 & 82.76 & 92.97 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the results of the backbone and proposed model on datasets.  
Figure 8: Examples of images in FMD for a class selected according to the result of the confusion matrix. (**a–d**) mean class glass, metal, stone, and wood examples, respectively. The black letter below the image indicates actual class, and the red letter indicates predicted class by the proposed model.  
Figure 9: Examples of class images in FMD. (**a–c**) are class water, metal, and wood examples, respectively.  
The backbone has a structure that minimizes the information loss as the model deepens. However, this was limited to classifying the detailed patterns of texture images and improving their performance. Therefore, the experimental results of structures that combine FRM with the backbone, such as Block 14, showed improved performance. Moreover, it showed that the FRM assisted in identifying and classifying image patterns of DTD, FMD, and KTH-TIP2b in terms of effectiveness.
## 4 Results and Discussion
### Error Rate  
Error rate values are presented in Table 8. It is consistently seen that the 600 system calls sequence is the first one that generates significantly better results than the smaller ones.  
The best results (the smallest number of error rate) equal to 0.007 were obtained by static (AWSCTD-CNN-S) and dynamic (AWSCTD-CNN-D) models. The best value of the FCN family was  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Count**} & **AWSCTD-CNN-S** & **AWSCTD-CNN-LSTM** & **AWSCTD-CNN-GRU** & **FCN** & **LSTM-FCN** & **GRU-FCN** \\ \hline
10 & 0.368 & 0.324 & 0.3  
equal to 0.011 for FCN and LSTM-FCN models. The differences between families are of no significant statistical difference and do not demonstrate the superiority of dual-flow models.
## 2 Optical Inspection Technologies in Railways
### Sensor Systems
#### 2.3.2 Track  
Damage that occurs can be divided into three large groups. Defects during manufacture (tache ovale or kidney defect); damage due to improper handling, installation, or use (e.g., wheel burn due to spinning wheels); and damage due to exhaustion (e.g., head cracks and squat defects) [55].  
For track geometry, MERMEC offers a measuring system that successfully deliver all critical rail geometry parameters (track gauge, cross level/cant, twist, alignment and longitudinal level, mid-chord offset, and optional parameters) at speeds of up to 400 km/h [22]. Unfortunately, there is no information on the achievable accuracy or resolution. With SOKOL, TVEMA offers a measuring system for recording track geometry up to a speed of 250 km/h. This speed can be achieved by combining optical triangulation and inertial measurement [24]. The TRACK-V2 from SelectraVision also works without contact and measures both the profile and the wear of the track [25]. Fraunhofer IPM offers a measuring system based on cameras and line-lasers. The sensors are arranged transverse to the direction of travel, are permanently mounted on the vehicle frame, and detect the track geometry based on laser light-section technique (see Figure 5). Depending on the design of the system and its distance from the track; sub-millimeter accuracy can be achieved with this system [23].  
In general, it is important to pay attention to which parameters are recorded in detail. The specifications vary considerably from provider to provider. Here, too, only superficial or geometric deviations can be detected. Faults within the track are not detected. Mounting underneath the vehicle can lead to soiling of the camera openings. These must be cleaned regularly in order to capture usable images. Depending on the manufacturer, different automatic cleaning variants are available.  
Figure 4: Main types of cracks in concrete sleepers according to [50]. Upper illustration shows that cracks near the rail seat cannot be detected in the built-up state.
## 2 Materials and Methods
### Vectorization Process  
FIR filters are commonly used in the first signal processing stage; however, such filters have several drawbacks. For instance, their implementation can introduce a phase shift and modify the morphology of the signal in a manner unsuitable for the succeeding process. Moreover, they have a computational cost at least proportional to \((m\times n)\) for kernel and data sizes \(m\) and \(n\), respectively. Considering that an ECG is essentially a graphical representation of the electrical activity in the heart, we apply common simplification methods from computer graphics, such as polyline simplification, which has a computational complexity of \(O(n)\) and reduces the amount of data to a fraction of \(n\). We call this the vectorization process because it employs a concept similar to that of converting bitmap images to vector images in computer graphics. Figure 3 illustrates the vectorization process.  
In vectorizing the signal data, we first define 11 slope ranges (Table 1), each having a threshold based on the peak-to-peak voltage of the ECG signal as follows:  
\[Vpp=0.65 Vpp_{t}+0.35 Vpp_{t-1} \tag{1}\]  
where \(Vpp_{t}\) is the maximum peak-to-peak voltage of the ECG signal over 2 s, and \(Vpp_{t-1}\) is the previous value of \(Vpp_{t}\).  
The vectorization algorithm combines consecutive lines with the same slope range into a single straight line (Figure 3). This method results in a reduced number of data points and a reduction of unnecessary signal fluctuations while preserving the data features. The resulting lines are encoded using 11 alphabetic symbols.  
Figure 3: Vectorization process.  
Figure 2: Block diagram of ECG signal processing.