esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Thematic Taxonomy of UWSNs
### Architectural Elements
#### 6.1.1 Sensors  
A hybrid architecture consists of both static and mobile sensors. In a two-dimensional (2D) space, the static sensor node is generally mounted on the seabed and communicates with a sink node for data transmission through multi-hop communication within multiple clusters [5]. However, a static three-dimensional (3D) architecture has a slightly different setup, in which sensors are deployed according to different depths by adjusting the length of the wire connected to the anchor on the sea bottom, with the support of inflated buoys. In the mobile architecture, the sensor nodes can move freely, enabling the dynamic change of the network topology. The mobile node requires two transceivers to maximize network capabilities for data collection. It can consist of remotely operative underwater vehicles (ROVs), autonomous underwater vehicles (AUVs), or sea gliders. The third type is a hybrid architecture that combines static and mobile sensor nodes to perform specific tasks [21]. In a hybrid system, mobile nodes can act as a router or controller to communicate with static or standard sensors for data sensing.  
UWSNs can improve monitoring and prediction activities, particularly on the ocean. For this reason, the coordination of technology and hardware is required to achieve the latter goal. Previous studies and experiments have shown that a combination of AUV and underwater sensors allow the implementation of observation or monitoring applications at different depth levels of water [8]. In [22], states that the integration between the sensor and AUV requires a network coordination algorithm to accomplish adaptive sampling and self-configuration. An adaptive sampling is a strategy to controll mobile vehicles and move them around the covered areas to perform the data collection process. Self-configuration is a procedure for AUV intervention to detect holes in the network due to the failure of sensor nodes or channel destruction. The synchronization between sensor nodes and AUVs requires obstruction avoidance via coordinated routing design.
# Simulation Study on Nanofluid Heat Transfer in Immersion Liquid-Cooled Server
## 2 Model Description  
### Mathematical Model  
In this paper, a single-phase fluid model is chosen. That is, the nanoparticles in the nanofluid are considered to be uniformly distributed in the base fluid. The two are in thermal equilibrium. There is no relative slip velocity between the nanoparticles and the base fluid, and the fluid flow is considered to be incompressible steady-state laminar flow. Based on the above assumptions, the heat transfer control equation of nanofluid can be expressed as:  
Continuity equation:  
\[\frac{\partial}{\partial x_{j}}\left(\rho_{f}u_{j}\right)=0 \tag{1}\]  
Momentum equation:  
\[\frac{\partial}{\partial x_{j}}\left(\rho_{f}u_{i}u_{j}\right)=-\frac{ \partial p}{\partial x_{i}}+\frac{\partial}{\partial x_{j}}\left[u_{f}\left( \frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u_{j}}{\partial x_{i}} \right)\right] \tag{2}\]  
Energy equation:  
\[\frac{\partial}{\partial x_{j}}\left(\rho_{f}c_{pf}Tu_{j}\right)=\frac{ \partial}{\partial x_{j}}\left[\lambda_{f}\frac{\partial T}{\partial x_{j}}\right] \tag{3}\]  
where \(u_{i}\) and \(u_{j}\) are velocity components; \(x_{i}\) and \(x_{j}\) denote Cartesian coordinate components; \(p\) denotes flow field pressure; and \(T\) denotes temperature. The density \(\rho_{f}\), kinetic viscosity \(\mu_{f}\), specific heat capacity \(c_{pf}\), and thermal conductivity \(\lambda_{f}\) of the nanofluid are calculated according to the following equations [9; 10].  
\[\rho_{f}=(1-\alpha)\rho_{w}+\alpha\rho_{p} \tag{4}\]  
\[\mu_{f}=\left(1+0.025\alpha+0.015\alpha^{2}\right)\cdot\mu_{w} \tag{5}\]  
\[c_{pf}\rho_{f}=\left(1-\alpha\right)\left(\rho c_{p}\right)_{w}+\alpha\left( \rho c_{p}\right)_{p} \tag{6}\]  
\[\lambda_{f}=\frac{\lambda_{p}+(n-1)\lambda_{w}-(n-1)\alpha\left(\lambda_{w}- \lambda_{p}\right)}{\lambda_{p}+(n-1)\lambda_{w}+\alpha\left(\lambda_{w}- \lambda_{p}\right)}\lambda_{w} \tag{7}\]  
where the subscripts \(w\) and \(p\) denote the corresponding thermal properties of the base liquid and nanoparticles, respectively; \(\alpha\) denotes the volume fraction of nanoparticles; and \(n\) is the shape factor of the nanoparticles. Additionally, in this paper, nanoparticles are considered as regular spherical shapes, \(n=3\)[11].
## 2 Data Quality Analysis 1 (DQA1): Satellite Position Difference When Ephemeris Updates
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{GEO} \\ \hline PRN & 1 & 2 & 3 & 4 & 5 \\ Mean & 0.37 & 0.32 & 0.35 & 0.45 & 0.48 \\ std & 0.25 & 0.19 & 0.25 & 0.31 & 0.29 \\ \hline \hline  & \multicolumn{4}{c}{IGSO} \\ \hline PRN & 6 & 7 & 8 & 9 & 10 \\ Mean & 0.33 & 0.34 & 0.31 & 0.24 & 0.86 \\ std & 0.22 & 0.22 & 0.24 & 0.17 & 2.99 \\ \hline \hline  & \multicolumn{4}{c}{MEO} \\ \hline PRN & 11 & 12 & 13 & 14 & X \\ Mean & 5.37 & 6.83 & 0.44 & 0.46 & X \\ std & 8.77 & 12.95 & 0.66 & 0.67 & X \\ \hline \hline \end{tabular}
\end{table} 
Table 3: BDS DQA1 statistical results.
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 4 In-Band Sensor Network Requirements
### Summary  
In-circuit measurement and control, enabled by on-wire communication technology and real-time feedback works. In a recent substantial study, using in-circuit measurements and \"reverse path\" on-wire communications demonstrated that localized \(\overline{\text{DVO}}\) can produce results which exceed the nominal 3-6% improvements [36]. The remaining questions regarding this approach to voltage optimization revolve around \"cost of deployment\" and ancillary applications which can be leveraged to produce a more effective return on investment and logical business case.
# Water Extraction in PolSAR Image Based on Superpixel and Graph Convolutional Network
## 2 Materials and Methods
### Graph Construction and Classification  
#### 2.3.1 Graph Construction  
An image is in a regular grid structure where each pixel has four neighbors in its first-order neighborhood and eight in its second-order neighborhood. The regularity of the data in Euclidean space makes it simple to handle, which also can be expressed as graph structure data composed of several nodes and the edges of the adjacency relationship between them. Graph structure data can be expressed as G = (\(V\), \(E\)), where \(V\) is the set of graph nodes with its feature vector, and E is the set of edges connected between nodes, including the attributes of edges. Based on the result of superpixel segmentation, an undirected graph (the weight of the edge is the same) is constructed by connecting each center point superpixel with all the surrounding superpixels with shared segmentation edges as the adjacency relationship and taking the features extracted by each superpixel unit as the features of each vertex in the graph structure. The undirected graph reflects the adjacency relationship between superpixels, and each node corresponds to a meaningful real entity. It reduces the amount of calculation through superpixel generation, which is particularly important in high-resolution images.
## 3 The Heavy-Tailed Characteristic  
This section offers the heavy-tailed behavior and regular variational results of the E-WD. Probability distributions that are right-skewed and possess heavy-tailed behavior are very useful in providing the best description of the biomedical data sets. A probability model is called a heavy tailed distribution, if it satisfies for every \(p\)  
\[\lim_{y\to\infty}S(y,\psi,\lambda,\theta)e^{pX}\to\infty. \tag{14}\]  
Figure 1: Different PDF for the E-WD.  
Figure 2: Different CDF for the E-WD.  
**Theorem 1**.: \(\forall p,\psi,\lambda,\theta>0\)_, the probability distribution \(f(y,\psi,\lambda,\theta)\) that given in Equation (5) is heavy tailed distribution as \(y\rightarrow\infty\)._  
Proof.: Based on Equation (10), we can write that  
\[\begin{array}{rcl}\lim_{y\rightarrow\infty}S(y,\psi,\lambda,\theta)e^{p\chi}&=& \lim_{y\rightarrow\infty}e^{py}\left(\frac{e^{-y^{\beta}\lambda}\theta-1}{e^{ \psi}-1}\right)\\ &=&\frac{\lim_{y\rightarrow\infty}e^{-\left(2-e^{-\lambda y^{\beta}}\theta \right)+py}}{1-e^{-\psi}}\\ &=&\frac{e^{-(2-0)\phi+p\psi}}{1-e^{-\psi}}\\ &=&\frac{e^{\infty}}{1-e^{-\psi}}\rightarrow\infty.\end{array} \tag{15}\]  
An important property of the heavy-tailed probability distributions is called the regular variational property. This property implies that the tail of the distribution decays in a power-law fashion, with an exponent \(\Delta\) that determines how fast it decays. The larger the value of \(\Delta\), the slower the decay of the tail. The regular variational property has important implications for many areas of science and engineering, including finance, telecommunications, and network science. It allows us to model extreme events and rare occurrences more accurately and to estimate their probabilities more reliably. Here, we derive the regular variational property of the E-WD. According to [PERSON]'s theorem (see, Seneta [19]), the E-WD in terms of SF \(S(y,\psi,\lambda,\theta)\) is regularly varying, if it satisfies for every \(p\)  
\[\lim_{y\rightarrow\infty}\frac{S(py,\psi,\lambda,\theta)}{S(y,\psi,\lambda, \theta)}=p^{\Delta},\qquad\forall p,\Delta>0. \tag{16}\]  
where \(\Delta\) represents an index of regular variation.  
**Theorem 2**.: \(\forall p,\theta>0\)_, non-zero and finite, the probability distribution \(f(y,\psi,\lambda,\theta)\) that given in Equation (5) is regularly varying model._  
Proof.: Using Equation (10), we can write that  
\[\begin{array}{rcl}\frac{S(py,\psi,p,\lambda,\theta)}{S(y,\psi,\lambda,\theta) }&=&\frac{1-\frac{1-x}{1-e^{-\psi}}}{1-e^{-\psi}}\\ &=&\frac{1-e^{\left(e^{-\psi^{\beta}\lambda}\right)^{p^{2}}}}{1-e^{-\psi^{ \beta}\lambda}}.\end{array} \tag{17}\]  
Now, we can write  
\[\lim_{y\rightarrow\infty}\frac{1-e^{\left(e^{-y^{\beta}\lambda}\psi\right)^{ p^{\beta}}}}{1-e^{e^{-y^{\beta}\lambda}\psi}}=\lim_{x\to 1}\frac{1-x^{p^{ \beta}}}{1-x}=p^{\theta}. \tag{18}\]  
The expression in Equation (18) is non-zero and finite \(\forall p,\theta>0\). Thus, \(f(y,\psi,\lambda,\theta)\) that given in Equation (5) is a regular varying distribution and \(\theta\) is the index of regular variation.
# Experimental Investigation of the BIPV System under Sanluurfa Meteorological Conditions
## 2 Materials and Methods
### Experiment Procedure and PVsyst Simulation Study  
In this study, experiments were conducted over a span of 6 months, including February, March, April, May, June, and July of 2023, to determine the effectiveness of the BIPV application under the meteorological conditions of Sanlurfa. The micro inverter calculated  
Figure 2: Measurement instruments and schematic representation of the experimental setup. the monthly electrical energy produced by each panel and sent it to the DTU device for recording. After obtaining the experimental data, an analysis was conducted using the PVsyst software. With the software, the location of the experimental setup at Harran University GAP-YENEV Research and Application Center was marked on the map to simulate the meteorological conditions of Sanluurfa. The structure on which the panels were placed was drawn on the program according to its dimensions. For the two panels to face south in the initial orientation, the tilt angle of the first orientation was set to \(90^{\circ}\) and the azimuth angle to \(0^{\circ}\). For the second orientation based on the east facade, the tilt angle was set to \(90^{\circ}\) and the azimuth angle to \(-90^{\circ}\). For the roof direction, in the third orientation, both the tilt and azimuth angles were set to \(0^{\circ}\). In the simulation, as in the experiment, panels were chosen as three monofacial and one bifacial panels, each with a capacity of 335 watts. The panels on the roof and the east facade were positioned with no distance between them and the building wall. On the south facade, a gap of 11.5 cm was left between the bifacial and monofacial panels and the building wall, as in the experimental setup. For each panel, an individual Hoymiles brand micro inverter with a value of MI-350 watts was chosen. The surface areas of the panels were entered as equal to each other and as 1.67 m\({}^{2}\), as in the experimental setup. The simulation-based experiment was conducted to determine the annual production quantity over monthly periods (Figure 3). When the results from the experimental setup are compared with the production data in PVsyst, the results are close to each other, as shown in Table 2 in percentage terms. However, in March, the production in the experimental setup was lower than that in the PVsyst program because of unusual meteorological conditions (floods and heavy rains). Except for March, the calculated results were generally quite close to each other. Therefore, it can be concluded that the electricity production values in the PVsyst analysis are very close to the actual values unless there is an extraordinary situation.
# A Deep Learning Model to Forecast Solar Irradiance Using a Sky Camera
## 2 Data and Instrument
### Sky Image  
Sky images were obtained using a Total Sky Camera J1006 as shown in Figure 1a, which captured sky images every minute. A fish-eye lens as the angle projection was then attached to the camera, which provides a 180\({}^{\circ}\) field of view in the horizontal direction. The camera system is based on a digital camera with a maximum resolution of 2272 \(\times\) 1704 pixels, and the images were stored locally in a computer at the Kookmin University. The camera system contains large-capacity silica gel drying cartridges to keep the inside of the instrument free of moisture. A ventilation unit was also applied to prevent the deposit of dust and dew on the optical system and to protect the sky camera against thermal heating effects.  
The sky images captured through the fish-eye lens were calibrated using a checkerboard calibration pattern to correct the fish-eye image. First, pictures were taken of the checkerboard in different positions and orientations, as shown in Figure 1b. Then, the MATLAB toolbox was used to detect the image and world points on the checkerboard as the calibration parameters. The image and checkerboard points were applied as the input for the image calibration in converting all the images to flat images.
## 2 Materials and Methods
### Analysis of the Ingredient from RGP  
The phenolic compound content was determined by the Folin-Ciocalteu reagent-based colorimetric assay. The absorbance was measured at 765 nm using a Thermo-Evolution 201 spectrophotometer (Fisher Scientific SL, Madrid, Spain). A calibration curve using Gallic acid as the reference standard was generated. The total phenolic content was expressed as the Gallic acid equivalents per sample (wet base) (mg GAE 100 g\({}^{-1}\)).  
Polyphenol oxidase (PPO) activity was measured at 420 nm and 25 \({}^{\circ}\)C for 3 min in a Thermo Scientific Evolution UV Vis spectrophotometer (Fisher Scientific SL, 187 Madrid, Spain), in a kinetic model. The results were expressed as a percentage of activity with respect to the control samples.  
The composition of the initial pomace was analyzed in 3 independent vacuum-packaged bags. For pH and water activity measurements of the pomace, a pHmeter Crison pH 25 + (Crison, Barcelona, Spain) and a Novasina (Labmaster, Lachen, Switzerland). The pH  
Figure 1: Weight loss (%) of dry-cured sausages during maturation.  
land) were used. Moisture and protein analyses were conducted according to the AOAC methodology [35]. The fat content was assessed by the Folch method [36] and fiber according to the modified Southgate method [37], all of them in wet base (WB). Fatty acid methyl esters (FAMEs) from the initial pomace were analyzed using an Agilent 6890 gas chromatograph (Agilent Technologies, Santa Clara, CA, USA), equipped with a flame ionization detector (FID) and a fused silica column (60 mm length, 0.25 mm inner diameter, and 0.25 m film thickness). The injector and detector temperatures were 260 \({}^{\circ}\)C and 280 \({}^{\circ}\)C, respectively. The column oven temperature was raised to 220 \({}^{\circ}\)C on a ramp temperature, and helium was used as a carrier gas, with a constant flow of 1.2 mL min\({}^{-1}\) and make-up of 25 mL min\({}^{-1}\). The injection mode was used with a split ratio of 1:100. Individual FAME identification was carried out on the basis of Sigma standards (Supelco 37 component FAME mix standard, Sigma Aldrich, St. Louis, MO, USA) compared with the retention times obtained. The results are expressed as a percentage of total fatty acid methyl esters.  
For microbiological analysis, 10 g of RPG was aseptically weighted in a sterile plastic bag and homogenized with 90 mL of a sterile solution peptone water (Merck, Darmstadt, Germany) in a masticator blender (Stomacher 400 Circulator, Seward, West Sussex, UK) at a 1/10 dilution (\(w/v\)). Mesophilic, molds and yeasts and _Enterobacteriaceae_ counts of RGP were determined. All microbial counts were expressed as the log of colony-forming units (CFU) per g of sample weight (log CFU g\({}^{-1}\)).
## 2 Methodology  
### VMD Based Decomposition Method  
VMD is a completely new, non-recursive signal decomposition method proposed by [PERSON] et al. in 2014 [32]. It can decompose any signal \(f(t)\) into K modal components \(u_{k}\) around the center frequency \(\omega_{k}\). For the VMD algorithm, the signal decomposition process is solving a variable division problem, which is modeled as follows  
\[\left\{\begin{array}{rl}min\\ \{u_{k}\},\{\omega_{k}\}&=\left\{\begin{array}{rl}\Sigma\\ \sum\limits_{k}u_{k}\end{array}\right.&\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\  
\[\hat{u}_{k}^{n+1}(\omega)=\frac{\hat{f}(\omega)-\sum_{i<k}\hat{u}_{i}^{n+1}(\omega )+\hat{\lambda}^{n}(\omega)/2}{1+2a\big{(}\omega-a_{k}^{n}\big{)}^{2}} \tag{3}\]  
\[\omega_{k}^{n+1}=\frac{\int_{0}^{\infty}\omega\big{|}\hat{u}_{k}^{n+1}(\omega) \big{|}^{2}d\omega}{\int_{0}^{\infty}\big{|}\hat{u}_{k}^{n+1}(\omega)\big{|}^{2 }d\omega} \tag{4}\]  
\[\hat{\lambda}^{n+1}(\omega)=\hat{\lambda}^{n}+\tau(\hat{f}(\omega)-\sum_{k} \hat{u}_{k}^{n+1}(\omega)) \tag{5}\]  
In Equations (3)-(5), \(\hat{u}_{k}^{n+1}(\omega)\), \(\hat{f}(\omega)\) and \(\hat{\lambda}^{n+1}(\omega)\) represent the Fourier transforms corresponding to \(u_{k}^{n+1}(\omega)\), \(f(\omega)\) and \(\lambda^{n+1}(\omega)\) respectively. The constraints for iterative stop are  
\[\frac{\sum_{k}\big{|}\hat{u}_{k}^{n+1}-\hat{u}_{k}^{n}\big{|}_{2}^{2}}{\|\hat {u}_{k}^{n}\big{|}_{2}^{2}}<\varepsilon \tag{6}\]  
Above all, the specific procedures of VMD are shown in Figure 1.
## 2 Materials and Methods
### Theory
#### 2.1.5 Lag-One Estimation  
In order to estimate the axial motion at each pixel in the image, a lag-one autocorrelation approach was used [2]. Using the beamformed, complex analytic signal \(s_{a}(\vec{r},t)\) at each pixel location, the complex slow-time autocorrelation was evaluated at a single sample lag via conjugate product followed by convolution with a rectangular filter, yielding  
\[R_{1}(\vec{r},t,\tau=\Delta t)=\text{rect}\left(\frac{t}{T}\right)*\left(s_{a }^{*}(\vec{r},t)s_{a}(\vec{r},t+\Delta t)\right), \tag{4}\]  
where \(R_{1}(\vec{r},t,\tau)\) is the autocorrelation estimate at location \(\vec{r}\), \(t\) is the slow-time instant, \(\tau\) is the lag, \(T\) is the averaging interval, and \(\Delta t\) is the sampling interval. The instantaneous phase shift is then estimated via an arctangent and converted to velocity via the Doppler equation, yielding  
\[\hat{\phi}(\vec{r},t) =\text{atan2}\left(\text{Im}\{R_{1}(\vec{r},t,\Delta t)\},\text {Re}\{R_{1}(\vec{r},t,\Delta t)\}\right)\] (5) \[\hat{\omega}(\vec{r},t) =\frac{\hat{\phi}(\vec{r},t)}{\Delta t}\] (6) \[\hat{\varrho}_{z}(\vec{r},t) =\frac{\hat{\omega}(\vec{r  
Due to the phase ambiguity of the arctangent function, possible values for \(\hat{\phi}\) are limited to the interval \((-\pi,\pi]\). Because of this ambiguity, the phase-based velocity estimation algorithms exhibit aliasing when the velocity magnitude exceeds the slow-time Nyquist rate, given by  
\[v_{N} \equiv\max\hat{v}_{z}=\frac{f_{N}}{f_{0}}\frac{c}{2} \tag{8}\] \[=\frac{1}{2\Delta tf_{0}}\frac{c}{2} \tag{9}\]  
where \(f_{N}=1/(2\Delta t)\) is the Nyquist frequency. For this reason, it is advantageous to utilize the smallest sampling interval possible when estimating high velocities using high-frequency ultrasound.
# Fast 3D Transmission Tower Detection Based on Virtual Views
###### Abstract  
Advanced remote sensing technologies leverage extensive synthetic aperture radar (SAR) satellite data and high-resolution airborne light detection and ranging (LiDAR) data to swiftly capture comprehensive 3D information about electrical grid assets and their surrounding environments. This facilitates in-depth scene analysis for target detection and classification, allowing for the early recognition of potential hazards near transmission towers (TTs). These innovations present a groundbreaking strategy for the automated inspection of electrical grid assets. However, traditional 3D target detection techniques, which involve searching the entire 3D space, are marred by low accuracy and high computational demands. Although deep learning-based 3D target detection methods have significantly improved detection precision, they rely on a large volume of 3D target samples for training and are sensitive to point cloud data density. Moreover, these methods demonstrate low detection efficiency, constraining their application in the automated monitoring of electricity networks. This paper proposes a fast 3D target detection method using virtual views to overcome these challenges related to detection accuracy and efficiency. The method first utilizes cutting-edge 2D splatting technology to project 3D point clouds with diverse densities from a specific viewpoint, generating a 2D virtual image. Then, a novel local-global dual-path feature fusion network based on YOLO is applied to detect TTs on the virtual image, ensuring efficient and accurate identification of their positions and types. Finally, by leveraging the projection transformation between the virtual image and the 3D point cloud, combined with a 3D region growing algorithm, the 3D points belonging to the TTs are extracted from the whole 3D point cloud. The effectiveness of the proposed method in terms of target detection rate and efficiency is validated through experiments on synthetic datasets and outdoor LiDAR point clouds.  
3D point cloud; 3D object detection; virtual view; 2D splatting; dual-path feature learning +
Footnote †: journal: Applied sciences  
0000-0001-5000-0000][PERSON]  
0000-0001-5000-0000][PERSON]  
0000-0001-5000-0000][PERSON]  
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0000-0001-5000-0000]China Electric Power Research Institute, Wuhan 430074, China; [EMAIL_ADDRESS] (L.Z.); [EMAIL_ADDRESS] (T.F.); [EMAIL_ADDRESS] (G.S.)