esa-sceva/satcom-chunk-collection huggingface dataset:
# Rainy Environment Identification Based on Channel State Information for Autonomous Vehicles
## 4 Vehicular Environment Identification Methodology
### The Proposed CNN Model  
To address the issue of vehicular environment identification, a Convolutional Neural Network (CNN) model is proposed, as shown in Figure 4.  
The CNN model constructed in this paper is based on one-dimensional convolutional layers designed to capture complex features from the Channel State Information (CSI) extracted from the V2V communication system. The model starts with two one-dimensional convolutional layers equipped with 64 (3 \(\times\) 1) filters, followed by LeakyReLU activation functions for non-linear transformations, enhancing the model's expressive ability for input data and preventing activation saturation. This is followed by a convolutional layer with 30 filters to further de  
Figure 4: Proposed CNN Model.  
Figure 3: Flow chart describing vehicular environment identification process.  
To improve the model's training stability and mitigate the effects of internal covariate shift, batch normalization layers are introduced after each convolutional layer. These layers normalize the activation values, ensuring rapid convergence and generalization performance of the network during training. Next, there is an average pooling layer with a pool size of 2, which helps reduce the model's complexity and increase its robustness.  
The deep structure of the model includes two additional one-dimensional convolutional layers, each equipped with 64 and 30 filters, followed by subsequent batch normalization layers. This design helps the model capture more refined features and provides NVIDIA GeForce GTX 1080 ample feature representation for classification tasks. Following the sequence of convolutional layers, the model contains three fully connected layers with 64, 128, and 5 neurons, respectively. The first two fully connected layers use ReLU activation functions, while the final output layer uses a SoftMax activation function to classify five different vehicular environments.  
To suppress overfitting, Dropout layers with a dropout rate of 0.5 are embedded after the first two fully connected layers. Additionally, all fully connected layers apply L2 regularization to further enhance the model's generalization capability. The entire network architecture is implemented using the TensorFlow library, with the model undergoing 20 training epochs and a batch size of 50 to ensure an optimal balance between classification accuracy and prediction time. The proposed model is built using the TensorFlow library and trained with 20 epochs and 50 batches, with the training process carried out on an NVIDIA GeForce GTX 1080 machine.
## 4 Case Study
### Prediction of Water Gushing Based on the Effective Radius of the Dynamic
#### 4.3.1 Defining Parameters  
The tunnel was divided into several calculation sections based on the characteristics of the water-bearing medium, the nature of the surrounding rock, the grade of the surrounding rock, the crossing of the fracture zone, the width and inclination of the fracture zone, as well as the construction schedule of the different surrounding rocks in the tunnel. The excavated and rock-disturbed part of the right tunnel were divided along the line into \(N\) sections, each with a length \(L_{n}\) of 10 m to 110 m. As the tunnel was excavated, \(N\) would continue to increase. Eventually the total length of the \(N\) segments would be equal to the actual length of the tunnel excavation. The amount of water gushing was recorded daily at the inlet of the right tunnel. The time was calculated from the first water gushing in the tunnel. The five study nodes were set as follows: (T) = 150 d, N = 23; (T) = 200 d, N = 24; (T) = 250 d, N = 25; (T) = 300 d, N = 26; (T) = 350 d, N = 27.  
According to the data from the engineering geological survey, the specific selection parameters for each section were shown in Figure 6. The selection of parameters for each segment was described as follows.  
The average thickness of the aquifers in each segment ranged from 11 m to 587 m. The specific yields of rock in each study section were from 0.001 to 0.025. The rainfall infiltration coefficients for weathered rock layers on the mountain surface were from 0.2 to 0.3. The average annual rainfall in the tunnel site area was 1865.6 mm.
# Deep Directional Network for Object Tracking
## 2 Related Work
### Deep Networks in Tracking  
Deep networks have been introduced into the target tracking field due to the deep feature semantic information description. In 2013, [PERSON] used the fully connected network based on a stacked autoencoder to develop the neural network-based target tracking [1]. Due to the lack of training data, the results were unsatisfactory. C-COT [8] uses a deep feature to directly replace the traditional artificial feature that caused C-COT to rank first in the list of VOT2016 challenge. Convolutional neural networks have been introduced to be more suitable for processing image problems. With the advent of large-scale video tracking datasets, such as VOT [3] and OTB [4], deep learning has a wide range of applications in target tracking [5; 22; 23]. However, the deep network's powerful capabilities arise from the large number of parameters and computational power of such networks. Many excellent tracking algorithms that use deep networks cannot be very fast. As an improved version of C-COT [8], ECO [24] aims to improve time and space efficiency. Although MDNet [5] tracking is very effective, the multi-instance mechanism makes the speed slow. In contrast to MDNet [5], we propose a single sample tracking mechanism to track the target, thus reducing the amount of network computing.
## 2 Materials and Methods
### External Validation  
To evaluate model performance, an independent external validation dataset comprising 55 patients was used for testing. The dataset, sourced from Addenbrooke's Hospital (Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK) and Edinburgh Royal Infirmary (NHS Lothian, Edinburgh, UK), was used to assess the model's predictive accuracy, clinical utility, and applicability to a more diverse population [38; 39].  
Patients were recruited into two cohorts: those with asymptomatic AAA and an age and sex-matched control group with atherosclerosis but no aortic aneurysm disease, confirmed by non-contrast CT-derived aortic diameters. All control patients were recruited in Cambridge, while patients with aneurysms were recruited from both sites. Inclusion criteria for the AAA cohort included age over 50 years and aneurysm size between 3.0 and 5.5 cm on ultrasound. Inclusion criteria for the control cohort required clinically stable cardiovascular disease for more than six months, defined as previous myocardial infarction, stroke, or peripheral vascular disease. Exclusion criteria for both cohorts included type 1 or type 2 diabetes with fasting glucose above 11 mmol/L, renal impairment (serum creatinine \(>\) 250 mmol/L), contrast allergy, or inability to provide informed consent.  
The external dataset used standardised image acquisition protocols (as per local Trust policies) and underwent identical processing, spatial resampling, VOI segmentation, and radiomic feature extraction (aligned to IBSI guidelines) as performed for the internal dataset. AAA growth rates were derived from serial ultrasound measurements, enabling direct comparison with internal dataset ground truths. Full details of image acquisition protocols can be provided upon request.
## 2 Experimental Data and Quality Control
The remaining variables, including diffuse fraction, D [46], k\({}_{\rm t}\)[47], k'\({}_{\rm d}\), [PERSON]'s brightness factor (\(\Delta\)) [48], and [PERSON]'s clearness index, \(\varepsilon\)[48], were calculated using the equations described in Table 2.  
The skies of the city of Burgos have been classified according to the ISO/CIE standard [41], which considers the angular distribution of luminance in the sky measured by the sky scanner. The sky is classified into 15 different types, where types 1 to 5 are considered overcast skies, types 6 to 10 are categorized as intermediate skies, and sky types 11 to 15 are identified as clear skies. A complete description of the ISO/CIE classification according to 15 types based on sky scanner measurements can be found in previous works [42; 43]. Figure 2a illustrates the frequency of occurrence (FOC) of each ISO/CIE standard sky type during the experimental campaign. Clear skies predominate in Burgos, with the most frequent sky type being classified as 13 (cloudless polluted with a wider solar corona). This sky type exceeds the 20% of all observed skies. When only the three main sky categories are considered, as shown in Figure 2b (overcast, intermediate, and clear), clear skies have the highest FOC (higher than 45%). This fact concurs with findings from previous studies conducted in Burgos [28]. Figure 1: Experimental facility used in this study (Higher Polytechnic School, University of Burgos, Spain). The box and whisker plot of Figure 3 shows the distribution of the GHUVE/GHI ratio based on 10 min data grouped by UTC hours, calculated from sunrise to sunset, using the complete database of the experimental campaign. The graph presents various statistical measures, including the mean value (gray crosses), the median (white lines inside the box), the interquartile range (the limits of the boxes), and both the maximum and minimum data values (the extreme whiskers), as well as the outlier values (black and gray circles). It can be observed that the hourly mean and median values of GHUVE/GHI gradually increased until noon (12 : 00 h) and then decreased until sunset. Higher dispersion of the values in the central hours of the day (10 : 00 h to 14 : 00 h) may be observed, as shown by the interquartile range (around \(9\cdot 10^{-3}\%\)). The maximum value (\(4.2\cdot 10^{-2}\%\)) is reached at 12 : 00 h UTC. Figures 4 and 5 show the statistical analysis of the 10 min data of the GHUVE/GHI ratio grouped by month and season, respectively. Figure 4 reveals a gradual increase of GHUVE/GHI until May, followed by practically constant values until August, and then a decrease for the rest of the year. The interquartile range fluctuated between \(3\cdot 10^{-3}\%\) and \(1.2\cdot 10^{-2}\%\) and the standard deviation ranged between \(3\cdot 10^{-3}\%\) and \(7\cdot 10^{-3}\%\). During the months from May to August, the ratio exhibited the greatest dispersion of the measurement campaign, with interquartile ranges around \(1.1\cdot 10^{-2}\%\) and standard
## 2 Methodology
### Forces and Moments on the Tubular Hybrid Joint  
The elastic model of Bigwood/Crocombe [24] satisfied the model considerations proposed in Section 2.1.2 and allows us to estimate the adhesive stress of the tubular hybrid joint. To apply these simplified formulas in the tubular hybrid joint, it is necessary to define the forces and moments, as explained below.  
In Figure 3, we considered that the reinforcement is subjected to forces (\(P\), \(V\), \(T\)) in three directions. In this study, the forces used in the formulas proposed by [PERSON]/Crocombe [24] are applied per unit length (see Section 2.1.2 and Appendix C).  
Where:  
* \(P\) is the force in the \(y\)-direction on the hybrid reinforcement; this force generates an equivalent moment \(M^{\prime}\) exerted in the tubular laminate. This moment is estimated as follows (see Figure 4): \[M^{\prime}=\frac{M}{6}=\frac{P\cdot D}{12}.\] (14)  
Figure 3: Applied forces on the hybrid joint panel and stiffener.  
\[V_{z}=\frac{V}{2},\qquad V_{y}=\frac{V}{2}\cdot\tan(30). \tag{15}\]  
The component (\(V_{y}\)) generates a moment in Adherent 1. The behavior of the tubular laminate in the \(y\)-direction is similar to that proposed by [PERSON]'s moment equation [18], see Figure 5b:  
\[M_{H-S}=V_{y}\cdot d_{1}\cdot k_{1}, \tag{16}\]  
in which:  
\[k_{1}=\frac{1}{1+\xi_{1}\cdot c+\frac{\left(\xi_{1}c\right)^{2}}{6}}, \tag{16a}\] \[\xi_{1}{}^{2}=\frac{12\cdot V_{y}\cdot\left(1-{\mu_{1}}^{2}\right)}{k_{b}\cdot E _{1}\cdot{h_{1}}^{3}}. \tag{16b}\]  
\(T\) is the force applied to the tubular laminate in the \(x\)-direction; this force is applied to both the laminates and the root to obtain the adhesive shear stress.  
Figure 4: The equivalent moment on the hybrid panel and stiffener.  
Figure 5: Equivalent lifting forces: (**a**) vertical force V decomposition, (**b**) lifting forces, and moment on the stiffener laminate.  
A typical application of the hybrid tubular joint between a steel deck and FRP panel of a superstructure is presented in Figure 6. The proposed methodology allows estimating the adhesive stresses, so it is necessary to define the forces (\(P\), \(V\), \(T\)), these forces must be calculated in advance to be applied over a tubular hybrid joint of the FRP panel, under pressure \(p\) (kN/m\({}^{2}\)).  
To estimate the resultant forces (\(P\), \(V\), \(T\)) on the pipes of the hybrid joint, a global analysis of a superstructure subjected to a pressure \(p\) is performed, additionally considering a heeling or pitching. The magnitude of \(p\) and the heel or pitch is recommended by the classification society.  
In cases where the hybrid joint is installed on the steel deck where there is auxiliary equipment, the moments transferred from the deck to the panel should be added in calculations.
## 3 Performance of IWP with Steel Anchor Box
### FE Analysis Results and Performance Comparison
#### 3.1.3 Maximum Principal Stress Distribution in Pylon Wall  
Figure 21 shows the distribution of principal tensile stress in the outer concrete of the pylon wall at the anchoring zones for the internal, exposed, and IWP designs. Figure 21a illustrates the outer surface of the pylon wall at Segment 3, with the origin of the coordinates located at the center of the outer surface of the pylon wall. For the exposed type, the principal tensile stress along the Y- and Z-axes is taken at x = 1.2 m on the pylon wall. Along the surface of the pylon wall, the principal tensile stress in the middle of the concrete's outer pylon wall for the IWP design is reduced by 1.5 MPa compared to the internal type, because of the fixed ends. For the exposed type, the maximum principal tensile stress on the pylon wall is maintained below 3.0 MPa due to the application of prestressing. Through the thickness direction of the pylon wall, for the IWP type, principal tensile stress no longer occurs beyond a depth of 0.6 m from the wall surface. For the internal type, principal tensile stress of approximately 1.0 MPa still appears on the inner side of the pylon wall. For the exposed type, principal tensile stress persists throughout the thickness of the pylon wall. This indicates that the IWP design, even without applying prestressing, can still improve the crack resistance of the concrete in the anchoring zone.  
Figure 21: Maximum principal stress of concrete pylon wall: (**a**) local coordinate; (**b**) along X-axis; (**c**) along Y-axis; (**d**) along Z-axis.
# Numerical Modeling of Scholte Wave in Acoustic-Elastic Coupled TTI Anisotropic Media
## 2 Wave Equations  
### Acoustic-Elastic Coupled Equation  
Following the form of Yu [35], we derive the 3D TTI acoustic-elastic coupled equation (see Appendix A):  
\[\begin{array}{l}\partial\frac{\partial}{\partial t}\mathrm{V}=\mathrm{L} \phi,\\ \frac{\partial}{\partial t}\phi=\mathrm{D}^{\alpha\epsilon}\mathrm{L}^{\mathrm{ T}}\mathrm{V}+\mathrm{f},\end{array} \tag{1}\]  
where \(\rho\) is density, \(t\) is time, \(\mathrm{V}=\left(\mathrm{v}_{x},\mathrm{v}_{y},\mathrm{v}_{z}\right)^{\mathrm{T}}\) is velocity vector of particle, \(\phi=\left(\mathrm{P}_{s},\tau^{\mathrm{s}}_{xx},\tau^{\mathrm{s}}_{yy},\tau^ {\mathrm{s}}_{zz},\tau^{\mathrm{s}}_{xy},\tau^{\mathrm{s}}_{xz},\tau^{\mathrm{ s}}_{yz}\right)^{\mathrm{T}}\) is a vector composed of synthetic pressure field and deviator stress, \(\mathrm{f}\) represents source, \(\mathrm{L}\) represents partial differential operator, \(\mathrm{D}^{\alpha\epsilon}\) is the stiffness coefficient matrix for TTI acoustic-elastic coupled equation, and \(\mathrm{D}_{ij}\) is the stiffness coefficient of TTI media.  
\[\mathrm{L}=\begin{bmatrix}-\frac{\partial}{\partial x}&\frac{\partial}{ \partial x}&0&0&\frac{\partial}{\partial y}&\frac{\partial}{\partial z}&0\\ -\frac{\partial}{\partial y}&0&\frac{\partial}{\partial y}&0&\frac{\partial}{ \partial z}&0&\frac{\partial}{\partial z}\\ -\frac{\partial}{\partial z}&0&0&\frac{\partial}{\partial z}&0&\frac{\partial }{\partial x}&\frac{\partial}{\partial y}\end{bmatrix}, \tag{2}\]  
and\[\mathrm{D}^{\mu\
u}=\begin{bmatrix}0&-\frac{D_{10}+D_{32}+D_{33}}{2}&-\frac{D_{32}+D_ {32}+D_{33}}{2}&-\frac{D_{32}+D_{33}+D_{33}}{2}&0&-\frac{D_{31}+D_{32}+D_{33}}{2 }&0\\ 0&\frac{2D_{32}+D_{33}+D_{33}}{2}&\frac{2D_{32}+D_{33}+D_{33}}{2}&\frac{2D_{32}+D _{33}+D_{33}}{2}&0&\frac{2D_{32}+D_{33}+D_{33}}{2}&0\\ \frac{2D_{32}+D_{33}+D_{33}}{2}&\frac{2D_{32}+D_{33}+D_{33}}{2}&\frac{2D_{32}+D _{33}+D_{33}}{2}&0&\frac{2D_{32}+D_{33}+D_{33}}{2}&0\\ 0&0&0&0&0&D_{44}&0&D_{46}\\ 0&D_{15}&D_{25}&D_{35}&0&D_{55}&0\\ 0&0&0&0&D_{46}&0&D_{66}\\ \end{bmatrix}. \tag{3}\]
## 2 Theory  
### Principle of Measurement  
From Equation (2), it can be derived that:  
\[\Delta n=n_{1}-n_{0}=\Delta L\ \frac{n_{0}}{L} \tag{3}\]  
where \(n_{0}\) is the initial measurement value of the air refractive index and \(n_{1}\) is the current measurement value of the air refractive index.  
So, the current value \(n_{1}\) of refractive index of air can be calculated as:  
\[n_{1}=n_{0}+\Delta n=n_{0}+\Delta L\ \frac{n_{0}}{L}=n_{0}\cdot\left(1+\frac{ \Delta L}{L}\right) \tag{4}\]  
According to this principle, a type of zero-expansion quartz material with the known coefficient of thermal expansion and its value close to zero, was selected to design and manufacture a standard etalon work-piece (as shown in Figure 1) with length \(L\). It was placed in the measuring light path in order to monitor the variation of the wavelength of the light path caused by the fluctuation of the environment during the measurement.  
The front-end face of the standard etalon was coated with reflective film as a reference arm, and the back end of the standard etalon was coated with reflective film as a measuring arm. The layout of the reference light path and the measured light path based on the etalon are shown in Figure 2[13].  
Figure 1: Standard etalon work-piece for measuring the refractive index of air.  
In Figure 2a, the beam is emitted from laser one, reflected from the reflector mirror two to beam bender mirror three, passed through the quarter wave plate eight, which is incident to measuring mirror four fixed on the front face of the tested piece six, returned from four and passed through two, reflected from two to four again through cube corner mirror five, and returned from four to form a reference beam through two. The measurement optical path in Figure 2b can be analyzed similarly.  
As shown in Figure 2, the measured beam and the reference beam pass through the same optical path in the interferometer. They are designed based on the concept of the common light path and symmetrical distribution of the interferometer. So, the measurement system has a heat balance function, that is to say, when the size of the interferometer varies with the temperature effect, the influence on the length measurements of the two light paths are cancelled out one another.
## Materials and Methods  
### Experiment Setup  
In the present work, the treatment system of flowing liquid with glow discharge in ambient air that was reported elsewhere was used [19]. The experiment setup is presented in Figure 1. Plasma discharge was generated in ambient air between needle electrode and liquid surface by applying positive high voltage to a needle electrode (anode). A grounded ring electrode (Ni wire, NI-311386, Nilaco, Tokyo, Japan) was introduced into the liquid, and during discharge, the grounded liquid was used as a cathode. The use of a needle electrode as anode and liquid as a cathode allowed us to avoid electrode erosion and minimize sample contamination by electrode metals.  
High voltage was produced by a custom power supply consisting of a regulated direct-current power supply (DC PSU), a push-pull generator, a high-voltage transformer, a diode rectifier, and a reservoir capacitor. The discharge gap between liquid surface and the pin of the needle electrode was set at 8.5 mm by using a micromanipulator.  
The voltage and current waveforms during the discharge were monitored using a high-voltage probe (P6015A, Tektronix, Beaverton, OR, USA) and a current probe (2877, Pearson Electronics Inc., Palo Alto, CA, USA), and stored in a digital oscilloscope (TBS2000, Tektronix Beaverton, OR, USA). The optical-emission spectrum (OES) from the plasma was recorded using a multichannel spectrometer (HR4000 GC-UV-NIR, Ocean Optics, Largo, FL, USA) and a quartz optical fiber.  
Liquid flow was employed to maintain the low temperature of the liquid (below 37 \({}^{\circ}\)C, which could be used for hydroponics and is safe for human body) after treatment and to produce larger PAM volumes under identical conditions. The treated liquid was introduced to the dish at a flow rate of 10.25 mL/min, and pumped out at a flow rate of 10 mL/min to compensate for liquid evaporation during the plasma treatment and keep the same level of liquid and discharge gap. The use of flowing  
Figure 1: Experiment setup.  
liquid allowed plasma stability to be ensured and provided a possibility to scale up production when the commonly used treatment of a single small-volume sample (mL scale) is only suitable for laboratory studies. Additionally, the treatment of small samples may result in deviations in PAM parameters (related to human errors and equipment accuracy) in the case of the preparation of a large volume of PAM by the collection of numerous small samples treated by plasma.  
In the present work, organic compound L-phenylalanine (L-Phe) was dissolved in 2 mmol/L (mM) phosphate buffer (PB) to a concentration of 80 mM and treated with plasma, with the following use in hydroponic plant growth or inactivation of bacteria. In all present experiments, we used a discharge gap of 8.5 mm, 28 W of input power supplied to the push-pull generator, and 10 mL/min liquid flow, which is referred in the present work as the standard condition.
# Design of MIMO Antenna with Double L-Shaped Structure for 5G NR
## 3 Results and Discussion  
Figure 4 depicts a fabricated prototype of the eight-element MIMO antenna system. To validate its effectiveness, the proposed design was simulated, fabricated, and tested. The simulations were performed using HFSS version 18, and the S-parameters of the fabricated prototype were measured using an Agilent Network Analyzer. Furthermore, the overall efficiency and radiation characteristics of the constructed system were evaluated in a microwave-shielded anechoic chamber.  
Figure 3: Surface electric current on the antenna ground plane when Ant1 is excited at 4.4 GHz. (**a**) Local current distribution. (**b**) Overall current distribution.