esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Euler Deconvolution and Analytic Signal  
The Euler deconvolution process [97] was applied on the magnetic data to locate the positions and corresponding depth estimates of magnetic anomalies [98]; we are interested in contact and discontinuities, otherwise, we processed the deconvolution with structural index of 0 to enhance the intended geological features. This depth estimation method is frequently used since it adopts no specific geologic model [99]. The map effectively presents the dispersal of deep and near-surface magnetic sources within the explored part (Figure 18). The results exhibited a range of depths between 92 and 1402 m that match closely with the results obtained by [3; 4; 5; 13] and were confirmed by the analytic signal spectrum (Figure 17B). In the predominant blue color zone (with depths less than 1400 m) situated mainly at the inlier center and spread over some portions within the suggested prospective area, the Euler deconvolution solutions support and match perfectly with the obtained results from the CET Grid and Porphyry Analysis.  
Figure 14: (**A**) Contact occurrence density map (structural complexity). (**B**) Orientation entropy map.  
Figure 16: (**A**) Amplitude contrast transform map. (**B**) Circular feature transform map with contouring of the existent and probable porphyry.  
Figure 15: Rose diagram of the obtained results.  
Figure 17: (**A**) Analytic signal map with contour boundaries of the existent and probable porphyry. (**B**) Depth estimation of magnetic sources by analytic signal spectrum.  
Figure 18: Euler deconvolution map for SI = 0.
## 3 Results and Discussion
### Electrostatic Potentials, Total Electron Densities, and Molecular Electrostatic Potentials  
The molecular electrostatic potentials (MEPs) of the title compound were calculated using the B3 LYP and HF methods in the gas phase, as shown in Figure 6. The MEP maps of (2_E_)-HIPC indicate that the regions of negative electrostatic potential lie outside of the compound and near the oxygen atom. For the imidazole ring of (2_E_)-HIPC, the negative electrostatic potential lies outside the ring.  
Therefore, no favorable location exists for the formation of a normal cation above the ring. The molecular electrostatic potential (red color) was located on the N and O atoms belonging to the imidazole ring and C=O group, respectively. Less negative values were observed on the N-H groups (blue color). Both regions should be different because they are the principal acceptor and donor sites of the H bonds. In the vicinity of the nitrogen and oxygen atoms, the charge magnitude varies from \(-0.008\) to \(-0.02\). In the benzene ring, the charges vary from 0.008 to 0.8 from the inner to the outer contour. The electrostatic potential of different atoms on the surface is represented by different colors. The value of the electrostatic potential increased from red (more negative) to blue (more positive). The coloring of these maps represents a range of \(-0.06023\)_a.u._ (deepest red) to 0.06023 _a.u._ (deepest blue) at the B3 LYP/6-311++G(d,p) level and \(-0.06321\)_a.u._ to 0.06321 _a.u._ at the HF/6-311++G(d,p) level. The atomic sites colored in the MEP map indicate the strongest attraction in blue and the strongest repulsion in red. The electrostatic potential of C17 is more positive (0.718286) due to the high electronegativity of the neighboring O19 atom. N18 is more negative (\(-0.638291\)) due to the nearby oxygen and intramolecular bonding of H39 with N15. All of the hydrogen atoms have positive potentials. C7 and C8 have positive potentials, and C9 has a negative potential in the side chain carbon atoms. All carbons in the rings have negative electrostatic potentials except for C14 due to its bonding with two nitrogen atoms.
## 6 Results and Discussion
### Case Study: Jeddah Sea Port  
Jeddah Seaport is the most important seaport in the Kingdom of Saudi Arabia. It has a strategic location in the middle of the Red Sea, it is located at 21\({}^{\circ}\)28' North Latitude and 39\({}^{\circ}\)10' West Longitude. Jeddah Seaport serves the two Muslim Holy cities Mecca and Medina in the Kingdom of Saudi Arabia (Jeddah Seaport, 2013). It occupies 10.5 square kilometers, with 58 deep water quays having an overall length of 11.2 kilometers with a draft reaching 16 meters [34]. Figure 17 shows a general overview of Jeddah Seaport and our study area.  
In order to prepare the required data for implementing the HybVOR approach, namely: (1) a Digital Elevation Model (DEM), (2) a Digital Surface Model (DSM) and (3) a 3D Model of Buildings, topographical surveys (based on a combination of GPS, Total Station and Level) were undertaken to acquire spatial data with the required accuracy for large scale plans (1:500).  
The first phase consists of exploring the area of interest and conducting a preliminary study of pertinent data to be acquired. Thereafter, 20 Ground Control Points (GCPs) were defined with a horizontal accuracy of 5 cm and a vertical accuracy of 3 cm. In order to determine the planimetric position of these GCPs, a differential GPS survey in rapid static mode for 10 GCPs was conducted. For the other 10 GCPs, a traversing method with total station was adopted because of the poor quality of GPS signal. In terms of GCPs elevation, a direct levelling method was used to measure differences in elevation. Then, the absolute values of elevations were calculated based on a reference point which has a known elevation in our study area.  
Once the horizontal and vertical coordinates of GCPs are determined, a detailed topographic survey of elements in the study area is carried out. These elements are relevant for performing the deployment of surveillance cameras according to the HybVOR approach. To acquire 3D details of buildings, a reflectorless total station was used (with a range of 500 m and a precision of 3 mm + 2 ppm). This type of total station facilitates acquiring details that are hardly or not accessible in the environment. Regarding the acquisition of elevation data related to terrain and streets, the indirect leveling method with total station was used. A regular grid of 5 m for ground elevation data is adopted because the terrain was quite homogenous.  
The elevation data corresponding to the ground were used for generating a Triangular Irregular Network (TIN) which will serve to create a raster DEM. 3D details of buildings are the basis for creating a 3D model of the study area. Such a 3D model has a major role in the HybVOR approach in terms of generating Voronoi diagram, producing DSM, performing raster based viewshed, and carrying out 3D Vector-based analysis of visibility.
# T-Move: A Light-Weight Protocol for Improved QoS in Content-Centric Networks with Producer Mobility
###### Abstract  
Recent interest in applications where content is of primary interest has triggered the exploration of a variety of protocols and algorithms. For such networks that are information-centric, architectures such as the Content-Centric Networking have been proven to result in good network performance. However, such architectures are still evolving to cater for application-specific requirements. This paper proposes T-Move, a light-weight solution for producer mobility and caching at the edge that is especially suitable for content-centric networks with mobile content producers. T-Move introduces a novel concept called _trendiness_ of data for Content-Centric Networking (CCN)/Named Data Networking (NDN)-based networks. It enhances network performance and quality of service (QoS) using two strategies--cache replacement and proactive content-pushing for handling producer mobility--both based on trendiness. It uses simple operations and smaller control message overhead and is suitable for networks where the response needs to be quick. Simulation results using ndnSIM show reduced traffic, content retrieval time, and increased cache hit ratio with T-Move, when compared to MAP-Me and plain NDN for networks of different sizes and mobility rates.  
Keywords:content-centric networking; quality of service; edge caching +
Footnote †: journal:
# An ADMM Based Parallel Approach for Fund of Fund Construction
## 3 Algorithm for FOF Optimization Model  
### ADMM Steps  
It is costly to solve the optimization problem (5) or (6) by calling the default solvers such as the fmincon(MATLAB). One key to solve the problem (3) is to separate the quadratic term and the nonlinear term, since there are effective solvers for quadratic problems. The augmented Lagrangian of (6) is  
\[L(x,z,u)=\frac{1}{2}\tilde{x}^{T}\tilde{P}\tilde{x}+\tilde{q}^{T}\tilde{x}+ \sum_{i=1}^{n}f_{i}(\tilde{z}_{i})+\mathcal{I}_{\mathcal{C}_{1}}(x)+\mathcal{I }_{\mathcal{C}_{2}}(z)+\frac{\tau}{2}||x-z+\frac{u}{\tau}||^{2}. \tag{7}\]  
Then, we develop  
The update of the variables \(x^{k}\) is \(z^{k}\) is obvious. We minimize the augmented Lagrangian function. In practice, we find that it requires many more iterations on large problems. The convergence of the ADMM algorithm is guaranteed under fairly general assumptions [22; 23]. Since the objective function is convex and the global solution exists, we have \(x^{k}\to x^{*}\), \(z^{k}\to z^{*}\) and \(u^{k}\to u^{*}\). For faster convergence, we suggest to perform the relaxed ADMM algorithm. The relaxed ADMM iterates \(x^{k},z^{k}\) and \(u^{k}\), for \(k=0,1,\dots,\) by  
\[x^{k+1} =\arg\min_{\bar{x}\in\mathcal{C}_{1}}\frac{1}{2}\bar{x}^{T}\bar{ P}\bar{x}+\bar{q}^{T}\bar{x}+\frac{\tau_{\bar{k}}}{2}||x-z^{k}+\frac{u^{k}}{ \tau_{k}}||^{2}+\mathcal{I}_{\mathcal{C}_{1}}(x), \tag{11}\] \[x^{k+1} =\gamma_{k}x^{k+1}-(1-\gamma_{k})z^{k}\] (12) \[z^{k+1} =\arg\min_{\bar{z}\in\mathcal{C}_{2}}\sum_{i=1}^{n}f_{i}(\bar{z} _{i})+\frac{\tau_{\bar{k}}}{2}||x^{k+1}-z+\frac{u^{k}}{\tau_{k}}||^{2}+ \mathcal{I}_{\mathcal{C}_{2}}(z),\] (13) \[u^{k+1} =u^{k}+\tau_{k}(x^{k+1}-z^{k+1}). \tag{14}\]  
Here, \(u^{k}\in\mathbb{R}^{m+n}\) denotes the dual variables on iteration \(k\). \((\tau_{k},\tau_{k})\) are sequences of penalty and relaxation parameters. If the problem (3) is solvable, then the sequence \((x^{k},z^{k},u^{k})\) converges to its primal-dual solution. On the other hand, if the problem is infeasible, then the sequence \((x^{k},z^{k},u^{k})\) does not converge, but the sequence \((x^{k}-x^{k-1},z^{k}-z^{k-1},u^{k}-u^{k-1})\) always converges and can be used to certify the infeasibility of the problem [24].  
Since the nonlinear function \(f_{i}(x)\) is separatable, we develop the ADMM-based algorithm and parallelize it on CUDA. The key point of ADMM iteration presented in (6) contains three steps: the solving of the linear equation, the minimum of the nonlinear problem (27) and the update of the dual variables \(\lambda\).
# Underestimates of Grassland Gross Primary Production in MODIS Standard Products
## 2 Materials and Methods
### CO\({}_{2}\) Eddy Flux and Meteorological Data  
We used the FLUXNET2015 datasets ([[http://www.fluxdata.org](http://www.fluxdata.org)]([http://www.fluxdata.org](http://www.fluxdata.org))) to evaluate the performance of GPPMOD. The FLUXNET2015 datasets include carbon dioxide (CO\({}_{2}\)), water vapor, energy fluxes, shortwave and longwave radiations, photosynthetically active radiation, temperature, and precipitation for each flux tower site. We selected all grassland flux tower sites across the world with more than five years of observations between 2000 and 2014 using the 500-m resolution. The selection included 15 grassland sites with eddy covariance towers. All of these were included in this study (Figure 1 and Table 1). The meteorological data including precipitation, air temperature, solar radiation, and _VPD_ were used in this study. We used the gap-filled GPP data derived from the Marginal Distribution Sampling (_MDS_) method for gap-filling and the night-time partitioning method for the net ecosystem exchange (NEE) partitioning (NEE) partitioning (NEE). The daily GPPEC values were transformed to monthly GPPEC values (g C m\({}^{-2}\) day\({}^{-1}\)) for a comparison with the GPPMOD.
## 5 Testing Environment and Configuration
### Testing Platform
The larger number of repetitions and increased transmission power explain the increase in time and energy required by the same device for sending and receiving the same messages.
# Image Transmission over Cognitive Radio Networks for Smart Grid Applications
## 3 Proposed MAC Layer for Image Streaming over CRSNs
### Primary and Secondary Users Behavior Models  
In this work, similar to existing works in [68; 72; 73; 82; 83], it is assumed that the time distance between PUs arrivals has an exponential distribution with the mean \(\mu_{i}\) (\(i\) is the channel number). Therefore, the arrival of PUs in the channel \(i\) has a Poisson distribution at the arrival rate \(\lambda_{i}=\frac{1}{\mu_{i}}\).  
In order to prevent the degradation of the primary network quality of service (QoS), the secondary nodes must perform SS before any transmission, and only send the channel when it is in IDLE state and recheck the status of the current channel during the sending process at specific times. In the case of a primary user arrival, the SU node should execute the handover operation (HO) from the current channel to release the channel capacity for an arriving PU. Additionally, all sensors are assumed to be equipped with a single interfaced module that can be switched between the existing S traffic channels as well as a common channel (CC). Here, it is assumed that the transmitter node always has a stream of images from its camera ready to be sent. Each image frame has \(I_{b}=r\times c\times d\) bits in which \(r\) is the number of rows in an input image, \(c\) the number of columns, \(d\) is the number of sub color channels and \(n\) is the depth of the bit (in our simulations, these parameters are considered to be 512, 512, 3, and 8, respectively).
## 7 Integration in a Virtual Reality Environment  
The VR app was created using the video game engine Unity. The decision to use this engine over others was based on it being one of the most popular engines for creating VR environments and experiences, which is due to it being extremely versatile, easy to use, and that it is supported across many different platforms. Like other graphics engines, Unity can also be used to create interactive applications. This fact makes the engine highly appealing to developers of interactive virtual environments.  
The VR app was designed to work with the headset Windows Mixed Reality (WMR). For this, researchers needed a computer with Windows 10 OS, Visual Studio programming software and SteamVR SDK. Unity includes integrated support for WMR, as well as a wide array of tools, packages and resources for creating cross-platform, interactive experiences. It is important to highlight four interesting features of SteamVR SDK for Windows Mixed Reality: (1) Visual interaction that allows the user to observe three-dimensional environments; (2) Movement capabilities that allow the user to move through the virtual environment through the use of a hand-controller, either by walking or by teleportation; (3) Scene selection that can be executed by clicking on special marks within the scene, or by pressing dedicated buttons for this purpose; (4) Presentation of text and audio data by selecting specific buttons (see Figure 16).  
hands and controllers to navigate in any direction and move closer to the finer details of the building. To make the VR application suitable for interactions, several C# scripts were implemented as features, allowing text labels to be viewed from different angles.  
Pointing at murals activates the markers that indicate there is additional information available (Figure 16b). When the user interacts with a marker, a text box containing factual information opens: for the altar murals, users are presented with historical data on the depictions, and for the presbytery murals, users are presented with information on the pictorial techniques and representations. Figure 16 displays a screenshot of the VR model in use and potential interactions.
# A Low-Cost Evaluation Tool for Synchronization Methods in Three-Phase Power Systems
## 1 Introduction
The main contributions of this work are (i) to propose a low-cost and versatile digital platform for the experimental evaluation of synchronization techniques, (ii) to present a methodology of its use for the experimental evaluation of synchronization techniques, and (iii) to illustrate the uses and advantages of the proposal via the experimental validation of synchronization techniques based on PLL. All of these contributions focus on power converters based on RESs for electric power injection. The remainder of this document is organized as follows: Section 2 presents the evaluation tool and methodology for the validation and evaluation of digital synchronization algorithms. Section 3 presents the main results when implementing the evaluation tool and evaluating two digital synchronization techniques based on PLL. Finally, Section 4 presents the main conclusions. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Tool** & **Equipment or Device** & **Price [USD]** & \begin{tabular}{c} **Price** \\ **Level** \\ \end{tabular} \\ \hline \multirow{6}{*}{\
\begin{tabular}{c} Emulated on Digital \\ Processing Systems (EDPS) \\ \end{tabular} } & Academic MATLAB per year & 275 & Upper Low \\ \cline{2-4}  & Startups MATLAB and Simulink Suite & 3950 & Medium \\ \cline{2-4}  & per year & & \\ \cline{2-4}  & STM32F4 Discovery, digital platform & 20 & Low \\ \cline{2-4}  & LAUNCHXL-F28379D, C2000 Family TI, digital platform & 50 & Low \\ \cline{2-4}  & PLECS RT Box CE + RT Box & 7000 & Medium \\ \cline{2-4}  & LaunchPad-Nucleo Interface & 24,000 & High \\ \cline{2-4}  & PLECS RT Box 2 + RT Box & & \\ \cline{2-4}  & LaunchPad-Nucleo Interface & 24,000 & High \\ \hline \multirow{6}{*}{
\begin{tabular}{c} Acquisition from \\ Electrical Systems (AES) \\ \end{tabular} } & GW Instek APS-1102 Programmable & 2490 & Medium \\ \cline{2-4}  & AC/DC Power Supply & 2490 & Medium \\ \cline{2-4}  & GW Instek PSU 6-200 Programmable DC & 1970 & Medium \\ \cline{2-4}  & Power Supply & & \\ \cline{2-4}  & ITECH IT7909-350-90 Regenerative & & \\ \cline{2-4}  & Grid Simulator & 31,000 & High \\ \cline{2-4}  & Programmable inverter TPI8032 & 20,130 & High \\ \cline{2-4}  & 22 kW, Imperix & & \\ \cline{2-4}  & Inverter 30A, 400 VAC, & & \\ \cline{2-4}  & MTL-Series, GUASCH & 1920 & Medium \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Prices of the equipment used to validate synchronization methods.
# Learning Approach for Angle Estimation Based on Characteristics of Phase Drift
## 4 Performance Evaluation
### Outlier Removal  
#### 4.2.1 Outlier Definition and Detection  
Outliers are exceptional data points that significantly deviate from the overall pattern of the dataset. These anomalies can arise due to various factors such as sensing errors, environmental interferences, or unexpected changes in the signal propagation path. In the context of IPSs, outliers can result from dynamic changes in the environment, such as the movement of obstacles, including people, which can cause sudden fluctuations in the received signal strength or phase drift values.  
In this study, we identified outliers using the Z-score method, which calculates how many standard deviations a data point is from the mean of the dataset. Initially, a threshold of two standard deviations was tested but was found to be too sensitive, identifying an excessive number of data points as outliers. Subsequently, a threshold of three standard deviations was applied, which provided a more appropriate balance between sensitivity and the retention of valid data points for this dataset. The \(Z\)-score for a data point \(x^{i}\) is calculated as follows:  
\[Z_{i}=\frac{x^{i}-\mu}{\sigma} \tag{10}\]  
where \(\mu\) denotes the mean of the dataset and \(\sigma\) denotes the standard deviation of the dataset. By applying this method to the phase drift values in the dataset, data points with  
\begin{table}
\begin{tabular}{c c} \hline
**Component** & **Specification** \\ \hline CPU & Intel Xeon 4215, 64-bit, 8 cores, 16 threads \\ Memory & 384 GB (64 GB \(\times\) 6) \\ GPU & NVIDIA Quadro RTX 8000 \\ GPU Memory & 48 GB \\ TensorFlowFlow & 2.9.0 \\ CUDA & 11.2 \\ CuDNN & 8 \\ \hline \end{tabular}
\end{table}
Table 1: Server configuration for model training.  
\begin{table}
\begin{tabular}{c c c} \hline
**Class** & **Parameters** & **Configurations** \\ \hline \multirow{4}{*}{Dataset} & Total samples & 379,995 \\  & No. of antenna & 6 antennas \\  & Antenna grouping & 2 groups of 3 \\  & Train:Val:Test & 7:1:2 \\  & Angle collection & 10-degree intervals \\  & Signal interval & 625 μs \\ \hline \multirow{3}{*}{Preprocessing} & Length/window & 100 \\  & Input size & (100, 2) \\  & Min–max scaling & Yes \\ \hline \multirow{4}{*}{Learning} & Learning rate & 0.001 \\  & Epoch & 15 \\  & Optimizer & Adam \\  & No. of GRU layers & 3 \\  & No. of dense layers & 7 \\  & Loss function & MSE \\  & Early stop & Yes \\  & Batch size & 32 \\ \hline \end{tabular}
\end{table}
Table 2: Experimental dataset and model training parameters.  
\(Z\)-scores exceeding the threshold of 3 were flagged as outliers and subsequently analyzed to determine their impact on the model's performance.
## 4 Architecture Design
### Emulation Environment  
The architecture and implementation for multi-access routing for moving vehicles based on continuous ML is tested and evaluated in an emulation environment. The following sections describe the creation of network and vehicle movement simulation data for emulation purposes. Additionally, mesh network architecture of the emulator is presented.
## 2 Materials and Methods
### Evaluation of Modeled ET Products Using GLEAM, MOD16A2, and TerraClimate  
To assess the performance of modeled ET products relative to established global datasets, GLEAM, MOD16A2, and TerraClimate, we compared daily, weekly, and monthly estimates with those from STEPS, SCOPE, and TSEBS. Annual ET maps were generated for each product to analyze spatial patterns and temporal variability. Seasonal comparisons were performed to capture the dominant ET regimes. Season window was determined based on the study that revised Mediterranean seasons using mean intra-annual variations of 12 key climatological parameters over 70 years of data (1948-2018) [46]. Given this study's one-year duration, only spring [21 March-12 June], summer [13 June-7 September], and autumn periods [8 September-23 November] were included. Winter was excluded due to the limited study timeframe.  
The evaporative stress index (ESI) was calculated for major land cover types to evaluate model behavior under different environmental conditions across regions using modeled ET products (ET\({}_{\text{a}}\)) and ERA5-PET.  
\[\text{ESI}=\frac{\text{ET}_{\text{a}}}{\text{PET}} \tag{5}\]