esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Instrument and Simulation Assumptions
### Simulation Assumptions
#### 3.2.2 Surface Model  
The surface reflectance has a significant impact on ALH information; hence, it is vital to choose the proper surface reflectance model. Many studies have suggested that compared with bare soil (bright surface case), surface types with relatively higher absorption (seawater, vegetation, etc.) show higher sensitivity to the ALH [1; 29]. However, the surface reflectance of different types is relatively low at the UV band, and the difference is small. For instance, we use the improved kernel-driven bidirectional reflectance distribution function (BRDF) model of vegetated surface, which has been proven to provide a better retrieval performance than the traditional Ross-Li model in the multi-angle polarization satellite observation retrieval framework [37]. For surface reflectance, the used BRDF is  
\[\rho_{S}(\mu_{0},\mu_{v},\phi,\lambda)=f_{iso}(\lambda)[1+k_{1}f_{geom}(\mu_{ 0},\mu_{v},\phi)+k_{2}f_{vol}(\mu_{0},\mu_{v},\phi)] \tag{11}\]  
where \(\mu_{0}\) and \(\mu_{v}\) represent the cosine of the solar zenith angles and the viewing zenith angle, respectively; \(f_{\text{iso}}\), \(f_{\text{geom}}\), and \(f_{\text{vol}}\) correspond to the isotropic, geometric-optical (Li-sparse kernel), and volumetric (Ross-thick kernel) surface scattering, respectively. \(k_{1}\) and \(k_{2}\) are the coefficients of the BRDF kernels at the given wavelength. In this paper, the surface reflectance \(f_{\text{iso}}\) was set to 0.0186, a value chosen from the surface reflectance databases USGS and ASTER [38; 39]. The values of \(k_{1}\) and \(k_{2}\) were chosen from [PERSON]'s work as equal to 0.087 and 0.668 [40].
## 3 Description of the Method
### Definition of the Sheer Curve
#### 3.4.2 Definition of the Sheer Curve in the Lateral View  
According to Figure 14a, the lateral view curve of the chine could be either a straight line or a slightly curved line. By the way, such a curve can be represented via a quadratic Bezier curve, as presented in Equation (33). The coordinates of the control point \(\mathbf{C}_{s3}\) are to be determined. According to the properties of the tangents at the endpoints of a quadratic B-spline curve, if the angular direction of the tangents at both ends of the curve is employed as a constraint, \(\mathbf{C}_{s3}\) can be obtained as the intersection point of the two tangents. However, improper values of the tangential angles \(\gamma_{0}\) and \(\gamma_{1}\) may result in the tangents intersecting at an unreasonable position, as illustrated in Figure 14b.  
\[\mathbf{S}_{s}(u)=B_{0,2}(u)\cdot\mathbf{P}_{s0}+B_{1,2}(u)\cdot\mathbf{C}_{s3 }+B_{2,2}(u)\cdot\mathbf{P}_{s1} \tag{33}\]  
On the other hand, designers are more concerned with the distribution of the height of the side projection of the chine curve along the longitudinal direction of the hull and the curvature of the curve. To this end, the coefficients \(\lambda_{s1}\) and \(\lambda_{s2}\) are introduced as shape coefficients, and Equation (33) is adopted to construct the relative position relationship of the control point coordinates \(\mathbf{C}_{s3}\), as demonstrated in Figure 15.  
\[\mathbf{C}_{s3}=\mathbf{P}_{s0}+(0.5+\lambda_{s2})(\mathbf{P}_{s1}-\mathbf{P} _{s0})+\lambda_{s1}(\mathbf{P}_{s1}-\mathbf{P}_{s0})\cdot\mathbf{R}_{y} \tag{34}\]  
Figure 14: The sheer curves in lateral view defined by angles: (**a**) normal condition; (**b**) unreasonable condition.  
where \(\mathbf{R}_{y}\) represents the matrix for rotating \(90^{\circ}\) around the \(y\)-axis.
## 5 Discussion  
### The Microscopic Cause of 'Pop-Ins'  
The minerals within the rock continuously undergo adjustments under the influence of constant loading and time effects. Isotropic rocks generally exhibit different creep properties due to their diverse mineral compositions [34]. However, the interaction between the matrix and structural plane also affects creep behavior for layered rocks. Therefore, even if the mineral composition is the same, factors like loading ratios [35], long-term strength [14], and micro-failure mechanisms [36] show significant anisotropy with varying foliation angles.  
The sudden increase in the creep curve of carbonaceous slate at specific intervals in Figure 4 (highlighted in red) was unique to layered rocks and is known as \"pop-ins\" [37]. [PERSON] et al. [38] attributed this phenomenon to localized fracture of the specimen during creep. The 'pop-ins' phenomenon was mostly observed during the deceleration creep stage at higher levels of creep loading.  
As observed in Figure 19a, the fracture surface at \(\beta=45^{\circ}\) exhibited a smooth appearance due to the creep stress corrosion. Noticeable abrasions were present on the fracture surface, providing evidence of the direction of slip. This indicated the gradual development of the damage over a considerable period of time. At \(\beta=75^{\circ}\), the creep effect caused a gradual and distinct exfoliation of the mineral surface, creating a step-like pattern, as shown in Figure 19b. The depth of exfoliation indicates the gradual increase in the loading level.  
These phenomena reveal the formation of 'pop-ins' from a microscopic perspective. As the loading ratio continuously increases, the microcrack surface along the foliation  
Figure 19: Macroscopic and microscopic fractures after creep failure.  
plane gradually becomes smooth. The pop-ins phenomenon for \(\beta\) = 45\({}^{\circ}\) is caused by the gradual slippage of the contact surface within a certain period of time. However, the pop-ins phenomenon at \(\beta\) = 75\({}^{\circ}\) is caused by the sudden exfoliation of minerals, resulting in instantaneous slippage.
# Assessment of Student Music Performances Using Deep Neural Networks
## 3 Material and Methods
### Deep Neural Network Architectures
#### 3.4.1 Fully Convolutional Pitch Contour Model  
General CNNs usually have a fully connected layer at the end that restricts their usage to fixed sized inputs and makes them unsuitable for direct use with music performance data. Simply replacing the fully connected layers of a CNN with convolutional layers removes this limitation. The resulting architecture is referred to as a fully convolutional neural network (FCN). FCNs have been used by the computer vision community for quite some time [57; 58]. However, their recent popularity is the result of the work done by [PERSON] et al., who used FCNs for performing dense pixel-level prediction for image segmentation tasks [59]. The ability to handle arbitrary length inputs makes FCNs a logical choice for use with music recordings [41; 42].  
The architecture details of the model are shown in Figure 4a. There are four convolutional layers, each of which has three components: (i) a 1D convolution layer, (ii) a 1D batch normalization layer, and (iii) a rectifier linear unit (ReLU) activation layer. Based on pilot experiments, for the first three layers, kernel size and stride are set to 7 (approx. 40 ms resolution in time) and 3 respectively. For the last layer, the kernel size is set to 35 and the stride is taken as 1. The number of features progressively increases to 16 in the 3 rd layer. Batch normalization is used to reduce over-fitting during training [60]. An average pooling layer is connected at the end to output a single assessment score irrespective of the length of the input PC. The only limitation of this kind of FCN architecture is that it assumes that there is a minimum length of PC, which, for our case, corresponds to roughly 6 seconds.
## 2 Technical Background  
### Savitzky-Golay Filter  
First introduced by [PERSON] and [PERSON]. [PERSON] in 1964, the so-called Savitzky-Golay filter is ideal for applications, such as signal processing and data analysis, which require the preservation of data shapes and features. This filter is commonly used in spectroscopy and signal processing as a preprocessing technique. It effectively reduces high-frequency noise in a signal owing to its smoothing capabilities. It is a digital filter that can smooth data points while maintaining the trend of the original signal. It operates by fitting small subsets of adjacent data points with a low-degree polynomial using linear least squares. This fitting is applied in a moving window fashion across the signal. Unlike other smoothing techniques, this filter excels in preserving the key features of the original signal, such as peak height and width, while effectively reducing noise.  
For a given signal measured at K points and a filter of width W, the filter computes a polynomial fit of order O within each filter window as it moves across the signal. The filter's estimate at the center of each window is derived from the polynomial fit at the central point. Typically, W is chosen as an odd integer for simplicity. A key requirement for the filter is that W must be at least O + 1, meaning that the number of measurements must be at least equal to the number of parameters to be estimated. If W is greater than O + 1, the filter provides smoothing to the signal; if W is equal to O + 1, no smoothing is applied. Therefore, increasing filter width W results in greater smoothing; however, it can also cause greater signal distortion. Similarly, higher polynomial orders can accommodate more complex shapes, although they may introduce distortions if the degree is too high relative to the window size. Therefore, optimal W and O are necessary for using this filter.
# Antioxidative and Photop Protective In Vitro Potential of Lavandula Angustifolium
###### Abstract
The initial phase in the investigation of medicinal plants involves the extraction process, which holds a pivotal influence on the overall effectiveness and reliability of the study's outcomes. The most classic methods of extraction include the following methods: Soxhlet extraction, maceration and hydrodistillation. However, these methods have certain limitations, which has led to growing interest in more modern and advanced extraction techniques. Among the most advanced and effective methods are ultrasound-assisted extraction, enzyme-assisted extraction, pulsed electric field-assisted extraction, microwave-assisted extraction, supercritical fluid extraction, and pressurized liquid extraction [33; 34]. The first method is based on the generation of ultrasonic waves that enhance the interaction between the plant material and the surrounding solvent [35]. In addition, this method allows the reduction in the time of the extraction process and it is also possible to apply a lower temperature in this process [36]. The use of ultrasound not only increases the extraction yield of the main product compared to other methods but also significantly speeds up the manufacturing process [37]. This method is considered environmentally friendly due to its minimal ecological footprint. Key variables to take into account include the choice of solvent, the type of plant material, the duration of extraction, and the temperature conditions applied [38]. The presented results of multicentre studies suggest that lavender is a source of many valuable ingredients that have found practical application. Therefore, it was decided to investigate the antioxidant and in vitro photoprotective potential of alcohol extracts from _Lavandula angustifolia_. The aim of the study was to evaluate the in vitro antioxidant and photoprotective potential of _Lavandula angustifolia_ extracts prepared in four short-chain alcohols applied in three concentrations using ultrasound-assisted extraction. To evaluate antioxidant activity, two frequently applied spectrophotometric methods, i.e., DPPH and ABTS, were used. Moreover, the in vitro sun protection factor (SPF) was determined. All of these assays were performed in extracts evaluated immediately after preparation and in the same extracts after twelve months. Additionally, in extracts obtained after a one-hour extraction, the qualitative identification of ingredients was performed.
# Positioning-Based Uplink Synchronization Method for NB-IoT in LEO Satellite Networks
## 3 Position-Aided Uplink Synchronization Method
### NPSS Detection
#### 3.2.1 Scenario 1: Three Satellites Visible
\begin{array}{ ccc}-(R+h_{n})^{2}-\mathbf{s_{1}^{T}s_{1}}&0&1\\ \Delta r_{1}^{2}-\mathbf{s_{2}^{T}s_{2}+s_{1}^{T}s_{1}}&2\Delta r_{1}&0\\ \Delta r_{2}^{2}-\mathbf{s_{3}^{T}s_{3}+s_{1}^{T}s_{1}}&2\Delta r_{2}&0\end{array}  \right)\left( \begin{array}{c}1\\ r_{1}\\ r_{1}^{2}\end{array} \right) \tag{24}\]
## 1 Introduction  
A wireless sensor network (WSN) is a large group of independent wireless sensor nodes comprised of sensors, microprocessors and communication modules. The objective of the WSN is to collect specific data and transmit it to the required destination. WSNs were originally studied for military purposes, but research is now focused on a wide range of consumer industries, giving rise to the notion of ubiquitous computing. Recently, new WSN approaches have been employed for more efficient traffic surveillance in intelligent transportation systems (ITS). Existing traffic information acquisition systems use wired power and communication and require powerful computing resources to attain high detection accuracy. However, such methodologies elevate the cost of construction and maintenance; consequently, the available detection area is narrowed. Thus, to measure traffic information across entire road networks, we require a low cost, highly scalable detection system.  
WSN-based traffic information acquisition systems satisfy these twin requirements of low cost and high scalability. However, a number of issues must be overcome before WSN techniques are applied to traffic information acquisition systems. As the sensor nodes in WSNs are typically very small and driven by batteries, their computing power is limited. Thus, it is important to minimize power consumption, and to reduce as much as possible the computational complexity and memory usage of the vehicle detection algorithm. Studies on the application of WSNs to traffic surveillance have considered magnetic, accelerometer and acoustic sensors. However, ultrasonic sensors have not been applied in such a WSN environment.  
Thus, in this paper, we introduce a power saving methodology for ultrasonic sensors and a low-complexity vehicle detection algorithm. Our approach does not compromise the vehicle detection accuracy. First, we analyze the characteristics of ultrasonic waves and sensors and then describe the vehicle detection methodology. Next, we discuss the power consumption of an ultrasonic sensor and provide detailed power-saving strategies. In addition, we introduce a novel detection algorithm that has low computational complexity and memory usage, while still providing an appropriate level of performance. The proposed vehicle detection algorithm is evaluated on real roads. Using the evaluation results, we discuss the possibilities and limitations of WSN-based traffic information acquisition systems with ultrasonic sensors.  
The remainder of this paper is organized as follows. In the next section, we review the background to traffic information acquisition systems and the related literature. In Section 3, we describe the characteristics of ultrasonic waves, and in Section 4, we analyze and model the process of vehicle detection using WSN-based ultrasonic sensors. Section 5 describes a case study, and Section 6 presents the results of a performance evaluation. Finally, our conclusions and ideas for future work regarding this research are given in Section 7.
## 2 Materials and Methods
### Data Acquisition and Data Analysis  
Data collection was carried out using a DJI Mavic 2 Enterprise Advanced UAV by SZ DJI Technology Co., Ltd. (Shenzhen, China). The characteristics regarding the applicability of this type of drone are related to the range of visibility that is provided, the possible speeds of movement, the capabilities of the camera, and the possibilities of use in windy weather. DJI Mavic 2 Enterprise Advanced is characterized by a speed of up to 50 kph in positioning mode, and the vertical and horizontal accuracy ranges are \(\pm\)0.5 m and \(\pm\)1.5 m, respectively. Its max flight time is between 24 and 31 min and it has resistance to wind speeds of up to 10 m/s. The gimbal has 3-axis (tilt, roll, and pan) stabilization, and the max control speed on a tilt is 120\({}^{\circ}\)/s with an angular vibration range of \(\pm\)0.005\({}^{\circ}\). The most important element for this study is the camera of the UAV. It uses a 3% CMOS 48 Mpx visual spectrum sensor with a resolution of 8000 \(\times\) 6000 px. For the needs of this study, the UAV has been set up to take photos with ISO 100, an 80% overlap, and the relative flying altitude and flying speed were 40 m and 30 km/h, respectively.  
The image processing and analysis have been implemented using the software ArcGIS Pro v. 2.8.6, developed by Esri Inc. (California, CA 92373, USA). This tool provides a wide range of options, such as combining overlapping images into a single orthomosaic map, generation of classification maps based on vegetation indices, machine learning, deep learning, etc. Furthermore, ArcGIS has numerous integrated tools for raster- and object-based analysis.  
Figure 2: Different types of degradation on the investigated pasture: shrubs and trees (**a**); stones and rocks (**b**); exposed soil and cattle tracks (**c**).
## 4 Conclusions  
This research aimed to address the challenges encountered while setting up a telecommunications IP network based on Asterisk PBX. The identified problems ranged from selecting the appropriate distribution kit and configuring the PBX and user equipment to ensuring secure transmission methods. These challenges can significantly hinder communication and development for small and medium-sized businesses.  
The solution proposed was a configuration that utilizes Blink SIP clients and an Asterisk Private Branch Exchange (PBX) server for communication. The results of this study demonstrated that this configuration is effective and cost-efficient for small and medium-sized businesses since it eliminates the need to purchase additional equipment. However, to ensure the security of data transmission, this study recommends the implementation of the TLS protocol, which provides encryption and authentication, thereby safeguarding the communication traffic.  
Solving issues related to Asterisk PBX configuration and offering a secure and cost-effective solution for small and medium-sized businesses, this research provides valuable information for improving the telecommunications infrastructure. The proposed configuration using Blink and Asterisk PBX can help enterprises expand their communication  
Figure 20: Transfer of encrypted media traffic.  
Figure 19: Transfer of encrypted session information.  
capabilities without significant financial investment. The integration of the TLS protocol safeguarding confidential information against unauthorized access and potential threats.  
This research contributes to the field of telecommunications by presenting a practical and secure approach to organizing IP networks, benefiting small and medium-sized businesses seeking efficient communication solutions. The results and recommendations of this research can assist decision-makers and specialists in choosing suitable communication setups and security measures, ultimately promoting business growth and productivity in the rapidly evolving landscape of modern telecommunications.  
Conceptualization, M.Y. and O.M.; methodology, M.Y.; software, M.Y.; validation, M.Y. and O.M.; formal analysis, A.M.; investigation, M.Y.; resources, A.B.; data curation, T.S.; writing--original draft preparation, M.Y., O.M. and T.S.; writing--review and editing, M.Y., O.M. and T.S.; visualization, O.M.; supervision, T.S.; project administration, T.S.; funding acquisition, M.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan, AP14871745, <<Development of a method for improving the security of a telecommunications network based on IP-PBX Asterisk>>.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results
### Network Architecture Results
#### 3.3.2 U-Net Results  
The 3D U-net architecture obtained a MAE of 89.54 HU, a PSNR of 25.69 and a Pearson Coefficient of 0.943 in the head dataset, which is the best result obtained for this dataset. However, the 2D scheme obtained a poor result of 117.21 HU for MAE, 23.26 for PSNR and 0.898 for Pearson coefficient in the head, being far away from the other two networks using 2D filters. According to the pelvis dataset, the 3D U-net obtained the best result among the 3D networks. Even so, it was around 1% worse than the best result for this dataset. Focusing on the 3D results, the U-net always had the best performance in both datasets. However, in the pelvis dataset, the U-net obtained a similar performance with statistically significant differences between the 2D and 3D MAE (all tissues: \(F_{2,56}=18.0\), \(p<0.001\), bone: \(F_{2,56}=4.9\), \(p<0.05\)) but not for PSNR (all tissues: \(F_{2,56}\)= 1.3, \(p=0.26\); bone: \(F_{2,56}=3.0\), \(p=0.09\)). In addition, the 2D scheme was slightly better for bone estimation.  
\begin{table}
\begin{tabular}{c c c c} \hline
**PSNR\(\backslash\)MAE** & **Residual-Net** & **Atrous-Net** & **U-Net** \\ \hline
**Residual-Net** & - & 0.22 & \textless{}0.01 \\ \hline
**Atrous-Net** & \textless{}0.005 & - & \textless{}0.05 \\ \hline
**U-Net** & \textless{}0.001 & 0.25 & - \\ \hline \end{tabular}
\end{table}
Table 28: MAE and PSNR \(p\)-values for 2D Pelvis. \(p\)-values of paired Student’s \(t\)-test for MAE and PSNR results with 2D architectures with the pelvis dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline
**PSNR\(\backslash\)MAE** & **Residual-Net** & **Atrous-Net** & **U-Net** \\ \hline
**Residual-Net** & - & 0.059 & 0.23 \\ \hline
**Atrous-Net** & 0.92 & - & 0.33 \\ \hline
**U-Net** & 0.15 & 0.17 & - \\ \hline \end{tabular}
\end{table}
Table 29: MAE and PSNR \(p\)-values for 3D Pelvis. \(p\)-values of paired Student’s \(t\)-test for MAE and PSNR results with 3D architectures with the pelvis dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **2D** & **3D16** & **3D32** \\ \hline
**Atrous-Net** & 9.6 (s) & 90.7 (s) & 18.9 (s) \\ \hline
**U-Net** & 7.1 (s) & 62.5 (s) & 14.4 (s) \\ \hline
**Residual-Net** & 7.7 (s) & 58.7 (s) & 11.4 (s) \\ \hline \end{tabular}
\end{table}
Table 30: Synthesis Times for Pelvis. Average time in seconds to synthesize a whole volume from the Pelvis Dataset using 2D networks and 3D networks reconstructing with stride 16 and 32.
# Beamforming Based on a SSS Angle Estimation Algorithm for 5G NR Networks
## 1 Introduction
### Notation  
A matrix \(\mathbf{A}\) is denoted by boldface capital letters, and the column vector \(\mathbf{a}\) is denoted by boldface lowercase letters. The operations trace, the conjugate, the transpose and the Hermitian of a matrix, are denoted by \(\text{tr}(.)\), \((.)^{*}\), \((.)^{T}\), and \((.)^{H}\), respectively. Finally, the index \(k\) represents the subcarrier, and \(\mathbf{s}\) represents the analog beam at the UE.