esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Analysis of EOD Helmet under Blast Load Events Using Human Head Model
## 1 Introduction
The main findings were that the front direction was the more injurious direction, and the pads played a significant role to protect by reducing the overpressure; however, it can increase the impulse inside the helmet. Our paper focuses on the blast protective helmet used in the explosive ordinance disposal (EOD) suit. This type of blast protective helmet has been hardly analyzed in any scientific literature [17; 18]. [PERSON] et al. [17] developed an experimental methodology to evaluate the performance of the EOD helmet against explosive blasts. The assessment of the risk of head and thorax failure was carried out using a Hybrid III dummy. [PERSON] et al. [18] also developed an experimental study for the assessment against explosive charges using a Hybrid III simulant. The authors acknowledge that the applicable standards for blast protection (NIJ 0117.01 standard) can be improved and that the Hybrid III does not faithfully represent the human head behavior under this type of loads. The current work includes the following contributions to the study of helmets for explosive threats: (i) A numerical study of explosions has been carried out on an approved complete assembly helmet currently operational in the Spanish police. (ii) A widely validated model of the human head-neck assembly has been used and adapted for use in simulations with explosives. (iii) The analysis of the influence of an EOD helmet on explosive threats has been introduced with injury criteria such as the center of gravity accelerations of the brain, the Gadd Severity Index (GSI), the Head Injury Criteria (HIC), or the pressure in the CSF, in addition to the commonly used ones (ICP, main deformations). (iv) A limit for the use of the helmet has been defined based on the widely accepted criterion of HIC and has been directly related to the severity of injuries produced.
# Automatic Modulation Classification Using Hybrid Data Augmentation and Lightweight Neural Network
## 3 Our Proposed AMC Method
### Details of the Lightweight CVResNet  
#### 3.2.1 Deep Residual Neural Network  
In neural network models, the depth of the network has a significant impact on the performance of the model. Deeper networks can perform more complex and powerful feature extraction, but this also leads to problems such as gradient disappearance, gradient explosion, and network degradation. To avoid these problems, this paper chooses the residual network as the feature extraction network for AMC. The residual network and the residual unit (Resunit) are shown in Figure 3. In the residual network, the Resunit extracts the features, the average pooling (AvgPool) reduces the feature dimension, and 'linear' is the fully connected layer that classifies the features to obtain modulated classification results.  
Figure 2: The framework of the lightweight CVResNet.
## 4 Results and Analysis
### Average Flow Characteristics  
In order to obtain the steady-state distribution characteristics of the jet space velocity, Figure 13 shows the time-averaged distribution of axial velocity. When \(x/D=0\), the direction of the axial velocity of the jet coincides with the direction of the negative y-axis. Compared with the 0\({}^{\circ}\) straight hole, the obstruction of the rotation spiral groove of the 45\({}^{\circ}\) spiral hole makes the absolute value of time-averaged axial velocity in the center hole large. However, the length of the jet core with them is relatively close. In the region of the jet space at \(x/D=0.5\), the exit airflow of the jet in the spiral groove contains a tangential velocity component, and the axial velocity of the jet rotates and spreads to the periphery, leading to a relatively small absolute value of the axial time-averaged velocity of the 45\({}^{\circ}\) spiral hole. In the case of \(x/D=1\) and \(x/D=2\), the axial time-averaged velocities of the 0\({}^{\circ}\) straight hole and 45\({}^{\circ}\) spiral hole in most areas of the jet space are close, but in the jet region of the wall, it can be seen that after the airflow of the 45\({}^{\circ}\) spiral hole hits the wall, the axial time-averaged velocity of the rebounding escape airflow is large, which is more conducive to the low-speed recirculation of the stagnation zone.  
Figure 14 shows the distribution of the root mean square of the fluctuating velocity in the axial and radial directions. There is less disturbance in the core area of the jet hole with a 0\({}^{\circ}\) straight hole, and the fluctuating velocity in the jet space basically maintains constant. The fluctuating velocity exhibits a complex change trend of increase-decrease-increase-decrease in the periphery region of the potential flow of \(x/D=0.5\), due to the addition of the swirling tangential velocity with a 45\({}^{\circ}\) spiral hole. In the region where the potential flow is far from \(x/D\geq 1\), the fluctuating velocity caused by the jet entraining the surrounding low-speed airflow is almost zero. In the area near the wall, due to the deflection of the velocity, it can be seen that the fluctuating velocity of the 45\({}^{\circ}\) spiral hole is larger than that of the 0\({}^{\circ}\) straight hole, and the turbulence on the wall with the 45\({}^{\circ}\) spiral hole is higher than that on the jet region on the wall. The flow transport capacity of the 45\({}^{\circ}\) spiral hole in the jet region of the wall is higher than that of the 0\({}^{\circ}\) straight hole, and the heat exchange capacity also becomes stronger.  
Figure 12: Power spectral density of radial fluctuating velocity near the target surface.  
Figure 13: The distribution of axial time-averaged velocity at different positions.
## 2 Materials and Method
### Asphalt Specimen Preparation
In order to effectively compare every property of the asphalt mixture, all aggregate gradation distributions are centralized within the range of one times the standard deviation for the intermediate interval. Since BOF slag is a substitute for coarse aggregate, and with the increased proportion of BOF slag, the passing percentage of coarse aggregate for every sieve will be decreased for the dense-graded mixtures. SMA and PA gradation would be formed to the jumping gradation (gap gradient) by \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sieve (mm)**} & \multicolumn{5}{c}{**BOF Replacement (\%)**} \\ \cline{2-5}  & **0** & **20** & **40** & **60** & **Criteria** \\ \hline
25 & 100 & 100 & 100 & 100 & 100 \\
19 & 97.9 & 93.9 & 99.7 & 99.0 & 90–100 \\
12.5 & 77.0 & 44.4 & 95.7 & 94.4 & - \\
9.5 & 72.0 & 38.0 & 78.3 & 79.6 & 56–80 \\
4.75 & 58.1 & 27.4 & 54.2 & 51.2 & 35–65 \\
2.36 & 39.2 & 20.1 & 33.0 & 36.1 & 23–49 \\
1.18 & 29.5 & 17.5 & 24.2 & 28.2 & - \\
0.6 & 21.9 & 15.5 & 18.4 & 21.5 & - \\
0.3 & 15.0 & 13.7 & 13.3 & 15.1 & 5–19 \\
0.15 & 8.6 & 12.0 & 8.7 & 9.2 & - \\
0.075 & 5.0 & 10.6 & 5.8 & 5.4 & 2–8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Sieve distribution (stone mastic asphalt (SMA)-graded mixture). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sieve (mm)**} & \multicolumn{5}{c}{**BOF Replacement (\%)**} \\ \cline{2-5}  & **0** & **20** & **40** & **60** & **Criteria** \\ \hline
25 & 100 & 100 & 100 & 100 & 100 \\
19 & 98.1 & 98.2 & 99.0 & 99.0 & 80–100 \\
12.5 & 79.4 & 82.8 & 94.2 & 93.8 & - \\
9.5 & 73.9 & 74.1 & 77.9 & 75.7 & 60–80 \\
4.75 & 58.1 & 63.4 & 60.9 & 48.4 & 48–65 \\
2.36 & 41.0 & 50.0 & 49.7 & 35.5 & 35–50 \\
1.18 & 31.6 & 39.3 & 39.4 & 27.8 & - \\
0.6 & 23.7 & 29.3 & 29.5 & 21.2 & 19–30 \\
0.3 & 16.4 & 20.0 & 20.2 & 14.9 & 13–23 \\
0.15 & 9.8 & 11.5 & 11.6 & 9 & 7–15 \\
0.075 & 6.0 & 6.6 & 6.6 & 5.3 & 0–8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sieve distribution (dense-graded mixture). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sieve (mm)**} & \multicolumn{5}{c}{**BOF Replacement (\%)**} \\ \cline{2-5}  & **0** & **20** & **40** & **60** & **Criteria** \\ \hline
25 & 100 & 100 & 100 & 100 & 100 \\
19 & 97.2 & 97.3 & 97.6 & 97.8 & 95–100 \\
12.5 & 68.4 & 69.2 & 71.1 & 73.4 & 64–84 \\
9.5 & 56.9 & 54.7 & 51.1 & 53.4 & - \\
4.75 & 24.7 & 25.1 & 23.3 & 25.0 & 10–31 \\
2.36 & 13.1 & 14.2 & 14.4 & 15.3 & 10–20 \\
1.18 & 10.8 & 11.2 & 11.7 & 12.4 & - \\
0.6 & 8.9 & 8.9 & 9.8 & 10.5 & - \\
0.3 & 7.2 & 6.7 & 8.1 & 8.8 & - \\
0.15 & 5.6 & 4.7 & 6.4 & 7.1 & - \\
0.075 & 4.7 & 3.5 & 5.2 & 5.7 & 3–7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Sieve distribution (porous asphalt (PA)-graded mixture). increasing the coarse aggregate and replacement; therefore, with the increased proportion of BOF slag, the passing percentage of coarse aggregate for every sieve and replacement (#200) will be reduced for both the SMA and PA mixtures.
## 6 Positioning Analysis  
To evaluate the performance of the calibration method, this section analyzes the positioning results from the investigated antennas using static PPP and kinematic PPK, with and without applying the antenna calibrations. The main focus is set to the height estimation. Results for the horizontal components can be found in the Appendix A. As mentioned before, only GPS \(L_{1}\) and \(L_{2}\) observations are used. The high-quality LEIAR25.R3 LEIT antenna serves as a reference for our analysis. The same model is also used as a base station antenna, which eliminates antenna-induced effects in the differential PPK processing. Unless mentioned otherwise, \(1^{\circ}\) resolution elevation antenna phase corrections are used.
###### Abstract  
This paper proposes an indirect method to measure absolute acoustic nonlinearity parameters using surface acoustic waves by employing a fully non-contact laser-ultrasonic technique. For this purpose, the relationship between the ratio of relative acoustic nonlinearity parameters measured using the proposed method in two different materials (a test material and a reference material) and the ratio of absolute acoustic nonlinearity parameters in these two materials was theoretically derived. Using this relationship, when the absolute nonlinearity parameter of the reference material is known, the absolute nonlinearity parameter of the test material can be obtained using the ratio of the measured relative parameters of the two materials. For experimental verification, aluminum and copper specimens were used as reference and test materials, respectively. The relative acoustic nonlinearity parameters of the two materials were measured from surface waves generated and received using lasers. Additionally, the absolute parameters of aluminum and copper were measured using a conventional direct measurement method, with the former being used as a reference value and the latter being used for comparison with the estimation result. The absolute parameter of copper estimated by the proposed method showed good agreement with the directly measured result.  
n pacs: 1
## 3 Materials and Methods
### Experimental Setup  
The experimental setup involved illuminating the samples horizontally polarized for SPR generation and collimated white light from a stabilized halogen light source (SLS301, Thorlabs, Newton, NJ, USA). The light was directed through a semi-transparent mirror, which allowed the illumination to reach the sample while enabling the collection of retroreflected light. The retroreflected light was captured using an interchangeable setup consisting of a spectrometer or a camera, as illustrated in Figure 5. For calibration, the reference light spectrum was recorded from a flat gold region (without the bi-grating) at normal incidence.  
Figure 4: AFM analysis of the grating surfaces. (**a**) Surface topography image of sample Bi-grating-6 (20 \(\upmu\)m \(\times\) 50 \(\upmu\)m) obtained via AFM. (**b**) 3D representation of the Bi-grating-6 surface profile, illustrating the depth and structure of the gratings. (**c**) Detailed surface profile measurement of Bi-grating-6.  
The reflected light was captured through backward scattering using a spectrometer (QE Pro, Ocean Insight, Orlando, FL, USA). SPR imaging was performed with a CMOS NIR camera (G-130 TEC1 Goldeye, Allied Vision, Stadtroda, Thuringia, Germany). The camera, capable of capturing wavelengths in the 400-1700 nm range, was utilized to observe the retroreflection and confirm the selective wavelength response of each bi-grating.
# Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms
## 2 Materials and Methods
### Satellite Absolute Dynamic Topography  
The sea surface geostrophic currents were obtained from the Copernicus Marine Service and are derived from optimally interpolated absolute dynamic topography data merging observations from a constellation of radar altimeters. Such a constellation is composed of four to six altimeters in the 2008-2019 temporal range [32]. The geostrophic currents are provided as daily fields with nominal \(1/8^{\circ}\) horizontal resolution. The 2008-2019 time series was extracted. The corresponding Copernicus Marine Service product and dataset ID are SEALEVEL_MED_PHY_L4_REP_OBSERVATIONS_008_051/dataset-duacs-rep-medsea-merged-allsat-phy-14, respectively (accessed on 1 March 2021 and now included as part of the SEALEVEL_EUR_PHY_L4_MY_008_068/cmems_obs-sl_eur_phy-ssh_my_allsat-14-duacs-0.125 deg_P1D dataset).
## 5 Discussion
### Methods: Algorithms and Machine Learning Models  
Figure 4 shows the distribution of algorithms. From these results it can be seen that the most commonly used algorithms are those based on ANN. Specifically, there are up to 118 works (around 69% of the total analyzed works) that use this machine learning model or any of its variants (Deep reinforcement learning (DRL), Extreme learning machine (ELM), Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), Back-Propagation Neural Network (BPNN), Capsule Neural Network (CapsNet), Stacked Denoising Autoencoders (SDA), Variational Autoencoder (VAE), Deep Belief Network (DBN), Recurrent Neural Networks (RNN), Multilayer Perceptron (MLP), Neural Network (NN), Single Multiplicative Neuron (SMN), and Deep Q-Networks (DQN)). Neuronal network-based algorithms are specially appropriate for nonlinear functions, and the fluctuating signal type of Wi-Fi fits perfectly into them.  
Figure 3: PRISMA flow diagram.  
The number of solutions based on ANN has been growing in recent years. In fact, by the year 2021, 20 out of 24 articles used ANN. In addition, the best result in the analyzed papers (mean error of 0.11 m) were obtained using a Deep Neural Network to process data from Wi-Fi mmWave signals [9]. However, as we will see later, these results alone do not indicate anything, as factors such as the size of the test area, number of APs, etc. affect these results.  
Several papers focus on combining different algorithms in order to choose the one that provides the best results in a particular case [21; 22; 23], while other papers focus on processing data collected from APs [24; 25; 26; 27]; finally, we found two papers [28; 29] that relied on applying a double algorithm, one to approximate the location and another to detail it more precisely from the first approximation.  
In the following subsections, we look more deeply into the specifications of NN, SVM, and RF.
# An Experimental Ultrasound Database for
## 3 Data Acquisition Protocol  
The proposed work aims to create a database of measured US scattered pressure fields for tomographic inversion purposes and to provide the scientific imaging community the opportunity to test the resolution capability of their approaches in some laboratory-controlled conditions with different levels of complexity.  
Therefore, in the present study, both the fields with and without the OIs located in the investigation area are collected, named \(u_{obj}\) and \(u_{emp}\), respectively. These two types of measures are performed in order to allow the evaluation of the scattered field \(u_{sct}\) by means of a subtraction operation, i.e., \(u_{sct}=u_{obj}-u_{emp}\).  
The acquisition protocol consists of a set of measurements per each scenario. More in detail, a stepped-frequency continuous-wave monochromatic signal was adopted as a source with a total of 41 equally-spaced frequency points in the range \([38,42]\) kHz with a sampling frequency of 500 kHz and a sixteen-bit precision. It is worth noting that the frequency step used to sample the considered band is equal to 100 Hz. Thus, adopting this choice guarantees a sufficiently large unambiguous distance, which for the considered settings is approximately equal to 1.71 m, which is suitable for the proposed setup.  
The sinusoidal pulse used as an active signal has a duration of 20 m per frequency and the procedure is repeated per each of the 4 transmitters of the circular array. Regarding the acquisitions, they are not simultaneous, but sequential, i.e., at each frequency one single transmitter is active and then one receiving channel acquires the data. In order to avoid transitional effects, the acquisition of each frequency includes a pause to ensure the collection of steady-state data. After that, the receiving operation is repeated and carried out sequentially for each receiving transducer, and then per each transmitter. It is worth noting that the use of a 20-m-length window per frequency ensures that the side lobes of sinc replicas that are in the band of interest are 90 dB lower than the power peak, making any low-pass filter unnecessary. The final time to complete the acquisition of each scenario (i.e., 41 frequency points, 4 views, 18 measures) is about 20 min.  
After the acquisition, the signal is filtered in the range \([38,42]\) kHz to reduce the effect of noise, and the mean beat signal is computed. Finally, due to the considered static scenes, an average value is determined for the whole observation window. Thus, at each frequency, a single complex value is obtained per transmitter-receiver pair.  
Figure 3: (**a**) Transducers frequency response in the band \([37,44]\) kHz and (**b**) radiation pattern at the frequency of 40 kHz. The pattern is approximately the same in both horizontal and vertical planes.
## 5 Results and Discussion
### Hydrocarbon Delineation in Seismic Scale  
After the model is validated in synthetic seismic, the analysis proceeded to real seismic data. The model was applied in \"X\" seismic Inline-731 and Xline-2744 where A-3 is located at. The red color in the hydrocarbon delineation result figures (see Figure 13 and Figure 15) indicates a potential hydrocarbon from the SOM. The model was compared with the conventional method where AVO cross-plotting was conducted to delineate potential hydrocarbons (see Figures 13-16). For validation purposes, a water saturation log is used for the indication of hydrocarbons at the well area.  
From the figures above, the SOM model managed to delineate hydrocarbons in Top I-35 Fm where it was confirmed from the water saturation log, indicating that the formation contains hydrocarbons. Compared with the conventional method by using AVO cross-plotting, the method could also delineate the hydrocarbons at the same area as the SOM does. However, the bias from the interpreter at defining the background of wet sands and shales and the outliers that are typically associated with a hydrocarbon reservoir might affect the delineation result. At the deeper interval around 1750 m, the SOM model indicate a potential hydrocarbon at the northwestern part of A-3. Unfortunately, due to a lack of well log data available, the delineation result could not be verified as to whether there is a hydrocarbon in that area.  
The model delineation result was analyzed in Top I-35 formation (see Figure 17). The hydrocarbon delineation results show in the right figure in Figure 17 where the red color indicates the potential hydrocarbon that was delineated by the SOM model. Unfortunately, it is difficult to conduct a quantitative interpretation in this view. Therefore, the A-3 well location is plotted to confirm the delineation of hydrocarbons since it has been validated from the previous analysis. From the figure above, the model shows that, at the A-3 well log position, hydrocarbon potential could be identified, which was shown as a red color.  
Figure 10: Hydrocarbon delineation result on gas condition.  
Figure 11: Hydrocarbon delineation on A\(-\)1.  
Figure 12: Hydrocarbon delineation on A\(-\)2.  
Figure 14: Hydrocarbon delineation result on “X” Seismic Inline—731 using AVO cross-plotting.  
Figure 13: Hydrocarbon delineation result on “X” Seismic Inline—731 using SOM.  
Figure 15: Hydrocarbon delineation result on “X” Seismic Xline—2744 using SOM.  
Figure 16: Hydrocarbon delineation result on “X” Seismic Xline—2744 using AVO cross-plotting.
# Optimizing Methanol Injection Quantity for Gas Hydrate Inhibition Using Machine Learning Models
## 3 Results and Discussion
### Analysis of Model Results
During training, it achieved an MAE of 3.648, corresponding with an average prediction error of 3.648 units, which was notably higher than the other models and suggested a poorer fit with the training data. In the testing phase, the MAE increased to 6.07, indicating a significant decline in accuracy when predicting methanol quantities for new pipeline scenarios. The RMSE values further highlighted KNN's limitations, escalating from 5.619 (training) to 10.475 (testing), reflecting a substantial increase in both the frequency and magnitude of prediction errors for the test set. Despite this, KNN's adj. R\({}^{2}\) scores of 0.996 (training) and 0.992 (testing) indicated that it still explained a high proportion of the variance in methanol requirements, although its explanatory power was the lowest among the models. KNN's inferior performance could be attributed to its reliance on local similarity, which struggled with the nonlinear relationships and high-dimensional feature space of this dataset, making it less suitable for precise methanol dosing predictions.
# Optical Cable Lifespan Prediction Method Based on Autoformer
## 3 Related Work
### Optical Cable Stress Calculation Method  
The term \"optical cable stress\" typically refers to external forces acting upon a cable, primarily originating from its installation and operating environment, significantly impacting its performance and lifespan.  
In accordance with the cable length formula, the correlation between cable length and stress under specific meteorological conditions is articulated as follows:  
\[L=l+\frac{g^{2}l^{3}}{24\sigma_{0}^{2}}, \tag{6}\]Consequently, under meteorological conditions \(m\) and \(n\), the optical cable lengths are expressed as follows:  
\[\left\{\begin{aligned} L_{m}&=l+\frac{g_{m}^{2}l^{3}}{24 \sigma_{n}^{2}}\\ L_{n}&=l+\frac{g_{m}^{2}l^{3}}{24\sigma_{n}^{2}} \end{aligned}\right., \tag{7}\]  
Substituting (7) into (5) results in the following equation:  
\[l+\frac{g_{m}^{2}l^{3}}{24\sigma_{n}^{2}}=l+\frac{g_{m}^{2}l^{3}}{24\sigma_{ m}^{2}}+\left[\alpha(t_{n}-t_{m})+\frac{1}{E}(\sigma_{n}-\sigma_{m})\right] \left(l+\frac{g_{m}^{2}l^{3}}{24\sigma_{m}^{2}}\right), \tag{8}\]  
Given that the term \(g_{m}^{2}l^{3}/24\sigma_{m}^{2}\) is negligible compared to other terms in the equation, it can be omitted. Through multiplying both sides of the equation by \(l/E\), (8) is simplified to  
\[\sigma_{n}-\frac{E\,g_{m}^{2}l^{2}}{24\sigma_{n}^{2}}=\sigma_{m}-\frac{E\,g_{ m}^{2}l^{2}}{24\sigma_{m}^{2}}-\alpha\cdot E(t_{n}-t_{m}), \tag{9}\]  
Equation (9) demonstrates that given stress \(\sigma_{m}\) under meteorological conditions \(m\) (specifically temperature \(t_{m}\) and load \(g_{m}\)), the stress \(\sigma_{n}\) under meteorological conditions \(n\) (specifically temperature \(t_{n}\) and load \(g_{n}\)) can be ascertained.