esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Problem Framework**} & \multirow{2}{*}{\begin{tabular}{c} **Load Demand** \\ **Uncertainty** \\ \end{tabular} } & \
\begin{tabular}{c} **Wind Power** \\ **Generation** \\ **Uncertainty** \\ \end{tabular} &
\begin{tabular}{c} **Objective Function** \\ **Uncertainties** \\ \end{tabular} & **Solution Methodology** \\ \hline
[3] & Deterministic, Multi-Objective & - & - & - & Active power losses, Total VAR cost, Voltage & MOEA \\
[4] & Deterministic, Multi-Objective & - & - & - & Investment cost, Short power voltage stability & MOEA \\ level, Transient stability level & & - & - & Active power losses, Voltage deviations, & VOA, DE, CWO, QOGE, \\
[5] & Deterministic, Single-Objective & - & - & - & Active power losses, Operating cost & SOEVO \\
[6] & Deterministic, Single-Objective & - & - & - & Active power losses, Operating cost & SPSO, A75D, EPSO \\
[7] & Deterministic, Multi-Objective & - & - & - & Transient cost, Short power voltage stability & MOEA \\
[8] & Deterministic, Single-objective & - & - & - & Active power losses, Voltage deviations, & VOA, DE, CWO, QOGE, \\
[9] & Deterministic, Single-Objective & - & - & - & Active power losses, Operating cost & SPSO, A75D, EPSO \\
[10] & Deterministic, Multi-Objective & - & - & - & Low loadability factor, Active power losses, VAR & CA \\
[11] & Deterministic, Single-Objective & - & - & - & Low loadability factor & GA \\
[12] & Probabilistic, Single-Objective & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & Fuel cost, Total cost & Mathematical Programming \\
[13] & Probabilistic, Single-Objective & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & VAR investment cost & Mathematical Programming \\
[14] & Probabilistic, Single-Objective & \(\checkmark\) & - & - & Variable investment cost & Multi-Objective Mathematical Programming \\
[15] & Probabilistic, Multi-Objective & \(\checkmark\) & - & - & Operating cost, VAR investment cost & Multi-Objective Mathematical \\  & & & & - & Modeling risk & Programming (\(\checkmark\)-consistent \\
[16] & Probabilistic, Single-Objective & \(\checkmark\) & - & - & Operating cost, VAR investment cost & GA \\
[17] & Probabilistic, Single-Objective & \(\checkmark\) & - & - & VAR investment cost & Mathematical Programming \\ Present Paper & Probabilistic, Multi-Objective & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & Active power losses, Total VAR cost, Voltage & Multi-objective Mathematical \\  & & & & & stability index, Loadability factor & Programming (\(\checkmark\)-constant \\  & & & & & stability index, Loadability factor & method) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between the previous research studies and the proposed method.
## 3 Results
### Quantitative Analysis of Observation Space
#### 3.3.2 Pairwise Observation Distribution Comparison
\begin{table}
\begin{tabular}{c c c c c c} \hline  & Breakout \#1 & Breakout \#2 & Breakout \#3 & Breakout \#4 & Breakout \#5 \\ \hline Breakout \#1 & 0.000 & 0.809 & 0.861 & 0.845 & 0.890 \\ Breakout \#2 & 0.809 & 0.000 & 0.879 & 0.843 & 0.905 \\ Breakout \#3 & 0.861 & 0.879 & 0.000 & 0.782 & 0.783 \\ Breakout \#4 & 0.845 & 0.843 & 0.782 & 0.000 & 0.792 \\ Breakout \#5 & 0.890 & 0.905 & 0.783 & 0.792 & 0.000 \\ \hline  & Enduro \#1 & Enduro \#2 & Enduro \#3 & Enduro \#4 & Enduro \#5 \\ \hline Enduro \#1 & 0.000 & 0.146 & 0.123 & 0.130 & 0.135 \\ Enduro \#2 & 0.146 & 0.000 & 0.143 & 0.141 & 0.125 \\ Enduro \#3 & 0.123 & 0.143 & 0.000 & 0.135 & 0.124 \\ Enduro \#4 & 0.130 & 0.141 & 0.135 & 0.000 & 0.135 \\ Enduro \#5 & 0.135 & 0.125 & 0.124 & 0.135 & 0.000 \\ \hline  & Freeway \#1 & Freeway \#2 & Freeway \#3 & Freeway \#4 & Freeway \#5 \\ \hline Freeway \#1 & 0.000 & 0.064 & 0.072 & 0.075 & 0.069 \\ Freeway \#2 & 0.064 & 0.000 & 0.066 & 0.071 & 0.072 \\ Freeway \#3 & 0.072 & 0.066 & 0.000 & 0.070 & 0.076 \\ Freeway \#4 & 0.075 & 0.071 & 0.070 & 0.000 & 0.067 \\ Freeway \#5 & 0.069 & 0.072 & 0.076 & 0.067 & 0.000 \\ \hline  & KFM \#1 & KFM \#2 & KFM \#3 & KFM \#4 & KFM \#5 \\ \hline KFM \#1 & 0.000 & 0.206 & 0.227 & 0.210 & 0.779 \\ KFM \#2 & 0.206 & 0.000 & 0.205 & 0.165 & 0.818 \\ KFM \#3 & 0.227 & 0.205 & 0.000 & 0.235 & 0.711 \\ KFM \#4 & 0.210 & 0.165 & 0.235 & 0.000 & 0.794 \\ KFM \#5 & 0.779 & 0.818 & 0.711 & 0.794 & 0.000 \\ \hline  & Pong \#1 & Pong \#2 & Pong \#3 & Pong \#4 & Pong \#5 \\ \hline Pong \#1 & 0.000 & 0.204 & 0.324 & 0.288 & 0.796 \\ Pong \#2 & 0.204 & 0.000 & 0.372 & 0.355 & 0.824 \\ Pong \#3 & 0.324 & 0.372 & 0.000 & 0.316 & 0.582 \\ Pong \#4 & 0.288 & 0.355 & 0.316 & 0.000 & 0.742 \\ Pong \#5 & 0.796 & 0.824 & 0.582 & 0.742 & 0.000 \\ \hline  & Qbert \#1 & Qbert \#2 & Qbert \#3 & Qbert \#5 \\ \hline Qbert \#1 & 0.000 & 0.831 & 0.084 & 0.698 & 0.782 \\ Qbert \#2 & 0.831 & 0.000 & 0.830 & 0.878 & 0.897 \\ Qbert \#3 & 0.084 & 0.830 & 0.000 & 0.736 & 0.781 \\ Qbert \#4 & 0.698 & 0.878 & 0.736 & 0.000 & 0.882 \\ Qbert \#5 & 0.782 & 0.897 & 0.781 & 0.882 & 0.000 \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline  & Alien \#1 & Alien \#2 & Alien \#3 & Alien \#4 & Alien \#5 \\ \hline Alien \#1 & 0.000 & 0.323 & 0.205 & 0.329 & 0.354 \\ Alien \#2 & 0.323 & 0.000 & 0.273 & 0.243 & 0.180 \\ Alien \#3 & 0.205 & 0.273 & 0.000 & 0.285 & 0.278 \\ Alien \#4 & 0.329 & 0.243 & 0.285 & 0.000 & 0.237 \\ Alien \#5 & 0.354 & 0.180 & 0.278 & 0.237 & 0.000 \\ \hline  & Boxing \#1 & Boxing \#2 & Boxing \#3 & Boxing \#4 & Boxing \#5 \\ \hline Boxing \#1 & 0.000 & 0.476 & 0.557 & 0.542 & 0.536 \\ Boxing \#2 & 0.476 & 0.000 & 0.363 & 0.219 & 0.195 \\ Boxing \#3 & 0.557 & 0.363 & 0.000 & 0.437 & 0.430 \\ Boxing \#4 & 0.542 & 0.219 & 0.437 & 0.000 & 0.074 \\ Boxing \#5 & 0.536 & 0.195 & 0.430 & 0.074 & 0.000 \\ \hline \end{tabular}
\end{table} 
Table 5: Pairwise Maximum Mean Discrepancy (MMD) between agent observation distributions.
# Machine Learning-Assisted Adaptive Modulation for Optimized Drone-User Communication in B5G
## 4 MI-Assisted Adaptive Modulation
### K-Means Clustering  
K-means clustering is a very powerful algorithm that falls under the category of unsupervised ML algorithms. The role of the algorithm is to cluster data points into \(K\) non-overlapping subsets called clusters.  
The k-means algorithm operates N data points, where each data point is represented by \(x\in\{1,2,..,N\}\). The goal is to find the association r, such that it minimizes the loss function,  
\[J=\sum_{n}\sum_{k}r_{nk}||\mathbf{x}_{n}-\mathbf{\mu}_{k}||, \tag{5}\]where  
\[r_{nk}=\begin{cases}1,&\text{if k}=argmin_{j}||\mathbf{x}_{n}-\mu_{k}||.\\ 0,&\text{otherwise},\end{cases} \tag{6}\]  
which means that the value of \(r_{nk}\) is set to 1, if the data point \(x_{i}\) is assigned to cluster \(k\) and 0 for other clusters. \(\mu_{k}\) is an n-dimensional vector that carries the location of the centroid of the cluster. The steps followed in K-means clustering in shown in Algorithm 1.
# Evaluation of the Acceleration Vibration Signal for Aggregates of the Horizontal Drilling Stand
## 4 Experimental Results and Analysis
### Vibration Signal Processing in the Frequency Domain
(**(a)** frequency amplitude spectrum of the vibration signal of three aggregates without drill bit, **(b)** the vibration signal of three aggregates without drill bit, **(c)** the andesite rock disintegrating vibration signal). power frequency spectra \(S_{xx}(\omega)\). By amplifying the amplitudes, the fluctuations in the vibration signal spectrum are reduced. The power spectra constructed by this method are shown in the figures (see Figures 11 and 12). If the power spectra are compared with the amplitude spectra in the figures in all modes, it is clear that the dominant frequencies have become visible, and the spectra have been cleared. The number of dominant frequencies has decreased. It is clear that some frequency components of lower power levels have been suppressed, highlighting more power-relevant components. These frequencies are in the reduced frequency range 0-4000 Hz (see Figure 11a-f and Figure 12a,b). In the IX. mode, the higher frequency band 6000-9000 Hz is of strong importance, which was marked as the band characteristic of andesite rock disintegrating (see Figure 12c). Figure 11: Power spectra by FFT algorithm ((a) frequency power spectrum of the vibration signal of the motor, (**b**) the vibration signal of the pump, (**c**) the vibration signal of the hydro-generator, (**d**) frequency power spectrum of vibration signal of motor and pump, (**e**) frequency power spectrum of vibration signal of motor and hydro-generator, (**f**) frequency power spectrum of the pump and hydro-generator vibration signal).
## 4 Results
Additionally, the positioning accuracy of the regional ionospheric model is similar to the post-GIM products. Among them, using the regional ionospheric model for PPP positioning can achieve a positioning accuracy of 1 m.  
To evaluate the influence of the regional correction model on the convergence time of smartphone single-frequency PPP positioning, this article sets up five experiments, each of them taking 10 min. The convergence condition is that the range of continuous 20 s is less than 0.5 m. It can be seen from Figure 8b that the ionospheric correction mainly affects the accuracy of the elevation direction; this article does not analyze the horizontal convergence time. What stands out in the Table 4 is that the regional correction model can shorten the convergence time in the elevation direction compared with the Klobuchar model, which proves that the regional ionospheric model can more accurately correct the ionospheric delay error of smartphones. It is of great significance for the high-precision positioning of smartphones. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Model** & **Horizontal (m)** & **Elevation (m)** & \begin{tabular}{c} **Promotion** \\ **(Horizontal)** \\ \end{tabular} & \begin{tabular}{c} **Promotion** \\ **(Elevation)** \\ \end{tabular} \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Carrier-smoothing \\ pseudorange \\ \end{tabular} } & No correction & 3.364 & 3.212 & \multirow{4}{*}{38\%} & 20\% \\  & Klobuchar & 2.758 & 2.586 & 18\% & 20\% \\  & Regional & 2.107 & 1.687 & 38\% & 48\% \\  & Post-GIM & 2.004 & 1.425 & 40\% & 56\% \\ \hline \multirow{4}{*}{
\begin{tabular}{c} PPP \\ \end{tabular} } & No correction & 2.427 & 2.698 & \multirow{4}{*}{30\%} & \multirow{4}{*}{31\%} \\  & Klobuchar & 1.708 & 1.874 & 30\% & 31\% \\ \cline{1-1}  & Regional & 0.836 & 1.081 & 65\% & 60\% \\ \cline{1-1}  & Post-GIM & 0.753 & 0.917 & 69\% & 66\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: RMSE of the positioning result. Figure 8: The positioning results of the Xiaomi 8 B smartphone. (**a**) Carrier-smoothing pseudorange positioning, (**b**) PPP (the no correction means no ionospheric correction).
# Opportunistic and On-Demand Network Coding-Based Solutions for LPWAN Forwarding
## 3 Challenges and Motivation  
### Preventing Unnecessary Forwarding  
One of the side effects of the forwarder mechanism is packet duplication caused by unnecessary forwarding. Unnecessary forwarding reduces the network capacity, which prevents packet transmission from an end-device, thereby leading to performance degradation. As LPWAN already facilitates broader coverage, the forwarder mechanism is only useful under certain conditions (see Figure 1).  
An LPWAN forwarder should consider any further forwarding as unnecessary once the gateway has already received the packet. To prevent unnecessary forwarding, we present an on-demand mechanism via feedback messages similar to SACK (selective acknowledgment), containing details about all currently received packets. Thereby, the relay only forwards the missing packets after receiving and checking the packet's state from the network server.
# A Baseband Wireless Spectrum Hypervisor for Multiplexing Concurrent OFDM Signals
## 9 Conclusions  
In this paper, we proposed a wireless spectrum hypervisor architecture that can abstracts a radio frequency (RF) front-end into a configurable number of virtual RF front ends. Our approach was proposed to improve spectral efficiency by efficiently using vacant gaps in congested spectrum bandwidths or employing network densification through infrastructure sharing. We provided a mathematical demonstration on how the proposed approach works and presented several simulation results proving its functionality and efficiency. Additionally, we presented an open-source and free proof of concept prototype of the proposed architecture and several experimental results validating its functionality and showing its performance.  
As future work, we highlight the following improvements to the proposed architecture prototype. As we could see, channelization and IFFT (i.e., OFDM modulation) processing tasks consume a lot of CPU time when 12 vPHYs are instantiated, therefore, offloading these tasks to the FPGA can increase both real-time and processing performance. Additionally, memory copy also consumes a lot of CPU time, and therefore, smarter ways of carrying out these copies should be investigated. The proposed architecture supports only multi-carrier-based waveforms; however, it would be interesting to add support to other kinds of waveforms, and therefore, another direction would be adding the support to non-multi-carrier waveforms. Finally, the integration of the proposed architecture's prototype with LTE or 5G-like upper layers is a direct sequence of the work presented here and would serve as a demonstration of what can be achieved with the proposed architecture in terms of real deployments.  
**Author Contributions:** Conceptualization, methodology, software, validation, investigation, formal analysis, data curation, and writing, original draft preparation: [PERSON]; writing, review and editing: [PERSON] and I.J.; resources, supervision, project administration, and funding acquisition: X.J. and I.M. All authors have read and agree to the published version of the manuscript.  
**Funding:** This work was funded by the European Union's Horizon 2020 research and innovation program under Grant Agreement No. 732174 - Orchestration and Reconfiguration Control Architecture (ORCA) project.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 UWOC System Model  
In the following sections, we will introduce our study as an innovative method for rapidly estimating bit error rate (BER) in technology. However, before doing so, we will provide concise explanations of two topics to help the audience understand the underlying challenges this study aims to address. These topics are (1) the digital signal evaluation cycle, in which the digital signal transforms from an optical digital signal on the transmitter (Tx) side to an electronic digital signal as an output of the optical receiver (Rx), and (2) the conventional BER estimation regimes, which include some familiar approaches: modified Monte Carlo (MC)-based estimation methods, the MC prediction method, and the Log-Likelihood Ratio-based BER model. The UWOC system generally consists of three fundamental components, as depicted in Figure 3: the transmitter unit, the water propagation channel, and the receiver section.  
The photons propagate across the water in the underwater communication channel independently from each other through any medium, facing different sequenced sets of optical events: transmission, absorption, and scattering (elastic and inelastic). The impact on the transmitted optical signal includes attenuation, temporal and spatial beam spreading,  
Figure 3: The layout of a typical UWOC system [62].  
deflection of its geometrical path, and amplitude and phase distortions [63]. Degradation, such as absorption and scattering, significantly impacts the UWOC's performance [59]. Turbulence is another degrading factor that causes beam spreading, beam wander, beam scintillation, and link misalignment. Oceanic water types can be classified as follows [64]: clean ocean water, pure sea water, turbid harbour water, and coastal ocean water. Furthermore, in turbid harbour water, several photons may arrive at the receiver with delays, intersymbol interference (ISI), and fading signal, reducing communication viability [65].
# An Enhanced Multi-Constraint Optimization Algorithm for Efficient Network Topology Generation
## 3 System Model and Problem Description  
### System Model  
In this paper, we employ a virtual topology strategy to accommodate the dynamic nature of optical communication constellation networks (OCCNs). The constellation network topology is represented as a time-varying graph, depicted in Figure 1. The dynamic topology relationships of the OCCN satellite nodes are discretized by dividing a complete satellite network operation period \(T\) into several time slices. The topologies of the optical communication satellite constellation network remain relatively stable within different time periods, referred to as the perception cycle \(I\), with \(I=ts-te\). Cycle \(T\) comprises a collection of perception periods, \(T=\{I_{1},I_{2},\ldots,I_{n}\}\). In light of the characteristics of OCCNs, we assume the following:  
1. Each satellite is assigned a unique identifier \(i\).
2. The communication channel between two satellites operates in full-duplex mode.
3. Every satellite has a finite number of optical transceivers, indicating the degree of connectivity \(d\).
4. Neighboring nodes are defined as other nodes within the communication radius of a satellite node, with which direct communication is possible.  
Based on time-varying graph theory, the optical communication constellation network (OCCN) can be modeled as a simple weighted graph \(G=(V,E,W)\). At a given time slot \(t=\{t_{1},t_{2},\ldots,t_{n}\}\), the set of vertices \(V=\{v_{1},v_{2},\ldots,v_{n}\}\) represents all satellite nodes in the constellation network. The collection of degrees for the satellite nodes, numbered as \(1,2,\ldots,n\), is denoted by \(D=\{\mathrm{d}_{1},\;\mathrm{d}_{2},\ldots,\mathrm{d}_{n}\}\).
## 4 Evaluation Results
### The Target Circuit and the Expected Behavior of the HT Mitigation Mechanism  
Example circuit-I performs ((a + b) \(\times\) c - d)/e arithmetic operations, occupying four PEs, and is deployed on a CGRA with a total of nine PEs. Example circuit-II performs (a)(a + b) \(\times\) (c + d) - m/(e + f) \(\times\) (g + h) arithmetic operations, occupies nine PEs, and is deployed on a CGRA with 25 PEs. The data flow diagrams (DFG) of the two circuits are shown in Figure 6.  
Examples of circuits infected by HT and corrected by CGRA reconfiguration are shown in Figures 7 and 8, where the blue arrows represent the data flow. Figures 7a and 8a show the initial version of the circuit, and Figures 7b and 8b show the infected version of the circuit with one PE infected with HT in circuit-I and two PEs infected with HT in circuit-II. After performing the EA and reconfiguring the CGRA, the expected circuit function is corrected as shown in Figures 7c,d and 8c,d.
# Real-Time 3D Reconstruction Pipeline for Room-Scale, Immersive, Medical Teleconsultation
## 1 Introduction
It directly extracts the mesh using an extended version of the recursive dual contouring algorithm [17] that works on voxelized point clouds instead of signed distance fields. With a voxelized point cloud, fewer surface samples have to be generated than volumetric signed distance samples by at least a factor of two using an optimal sparse data structure, where only corners of sign-changing cells are considered because the vertex generation for the cell interior is skipped. We can also further exploit the octree structure to efficiently simplify the resulting geometries with vertex clustering and connect mixed voxelization resolutions with dual contouring. Our contributions are as follows: First, we extend the recursive dual contouring algorithm by [PERSON] et al. [17] to include all diagonal connections for cells, which enables the algorithm to be used on point clouds stored in an octree. We define the hole-filling and connectivity criteria for the point-cloud-based algorithm to build correct surface meshes. Furthermore, we adapt vertex clustering simplification to include filtering conditions that preserve the topology and silhouette of nonmanifold, initial surface meshes. We evaluate the proposed method by comparing the reconstruction quality with existing methods and provide real-world performance measurements that show that the proposed pipeline can reconstruct room-scale environments in real time. The remainder of this document is structured as follows. In Section 2, we review related work and discuss differences between the proposed and existing methods. In Section 3, we present our proposed pipeline step by step. In Section 4, we introduce our extensions to apply dual contouring on voxelized point clouds and showcase common issues (Section 4.2) that generate interior meshes. Based on this observation, we present the filtering conditions to prevent interior mesh generation during simplification and a method to preserve nonmanifold edges in Section 5. In Section 6, we compare our reconstruction to existing offline direct triangulation and implicit surface reconstruction methods and present performance results for reconstructing a room captured with a ring of six RGB-D cameras. We finally conclude in Section 7 and present future work in Section 8.
## 2 Materials and Methods
### Spectral and Image Feature Extraction
#### 2.3.3 Image Feature Extraction  
Morphologic features were employed for variety classification in pioneer research [24; 25]. According to these previous study results, five morphologic features of each kernel, including area, circularity, aspect ratio, roundness, and solidity, were extracted from an image of 782.6 nm.  
Image texture, defined as a function of the spatial variation in pixel intensities, has been widely applied in the quality evaluation and safety inspection of food and agricultural products. Thus, image texture analyses based on first-order and second-order statistics were also extracted from hyperspectral images under selected wavelengths to improve classification accuracy. The first-order statistics provide information on the gray-level distribution of the image while ignoring the spatial interaction between pixels. The five first-order statistics parameters, namely, energy, entropy, skew, standard deviation, and moment, were extracted in this study. The second-order statistics, namely, contrast, correlation, energy, and homogenous derived from the co-occurrence matrix (GLCM), were calculated from the four directions (\(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\), and \(135^{\circ}\)) with displacement of \(d=1\). The average value and the standard deviation for each second-order static (contrast, correlation, energy, and homogenous, respectively), were finally obtained. Therefore, 13 texture features, including 5 first-order statistics and 8 second-order statistics, were extracted from each hyperspectral image, and finally 13 \(\times\) Nw (Nw is the wavelength number selected by SPA) texture features and 5 morphologic features were used as the images features in the following analysis.