esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data  
We used the data recorded via the CPIS station, which is installed 8 m from the Pisciarelli fumarole and mud poll. The station is equipped with a Guralp CMG40T broadband seismometer. The sampling rate is 100 samples per second. The signal is continuously transmitted to the data acquisition center of the Osservatorio Vesuviano (Istituto Nazionale di Geofisica e Vulcanologia--INGV). Previous studies have shown that the signal gen  
Figure 1: **(a)** Map of the Campi Flegrei caldera (the coordinates are in UTM). The seismic station CPIS, in the Pisciarelli area, is marked by a red triangle, east of the Solfatara crater. The dashed red line indicates the edge of the Campi Flegrei caldera. **(b)** Detail of the Pisciarelli hydrothermal area showing the locations of the CPIS seismic station, 8 m from the fumarole-mud pool axis (green line).  
erated via the fumarole-mud pool system, recorded via the CPIS station, is vertically polarized [22; 23], so the measurement of the fumarolic tremor amplitude is based on the vertical component of the CPIS station. Moreover, the frequency content of this signal is generally between 5 and 15 Hz, with a peak around 8-10 Hz [22; 23]. We selected two days of 2018 and eighteen days of 2020 to carry out our analysis. In particular, we chose 1 December 2018 as an example of a fumarolic tremor with SDEs (Figure 2a), and 15 November 2018 as an example of a typical fumarolic tremor signal (Figure 2b). Then, we considered the interval 25 August-11 September 2020, which contains the second episode of tremor with SDEs.  
To carry out the neural analysis, we divided the continuous signal into 1 min intervals stored in files (1440 files per day).
# Comparison and Combination of Mobile and Terrestrial Laser Scanning for Natural Forest Inventories
## 5 Results
### Automatic Tree Detection  
We used a minimum point number for the tree detection (cluster search) in the TLS data of \(n^{\text{TLS}}_{min}\) = 150 and a slice thickness \(h^{\text{TLS}}_{slice}\) of 0.07 m. Finally, all 24 trees were detected in the TLS dataset of plot 1 with three overdetected trees (commission error) (Figure 7). In plot 2, all 22 trees were detected in the TLS dataset. Due to the very high number of young understory trees (Figure 4) 30 commission errors occurred. A zoom-in figure of the point cloud of plot 1 is presented in Appendix A (Figure A1).  
The minimum number of points \(n^{\text{MLS}}_{min}\) and the slice thickness \(h^{\text{MLS}}_{slice}\) for tree detection in the MLS data were increased compared to TLS (\(n^{\text{MLS}}_{min}\) = 30, \(h^{\text{MLS}}_{slice}\) = 0.12 m) to avoid a rejection due to the smaller number of points in the horizontal section. Nevertheless, in plot 1 three trees were not detected (12.5% omission error) and eight commission errors occurred. These trees had a DBH \(<\) 10 cm and, as a consequence, there were not enough points in the horizontal sections. Figure 7a,b show that the TLS points represent a closed stem section and the MLS points only face the trajectory side of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Plot 1**} & \multicolumn{2}{c}{**Plot 2**} \\  & & **RMSE (cm)** & & **RMSE (cm)** \\ \cline{2-6}  & **Leaf-off** & **Leaf-on** & **Leaf-off** & **Leaf-on** \\ \hline \multirow{2}{*}{TLS} & 5 scan positions [1] & 0.15 & & 0.33 & 0.27 & 0.20 \\  & leaf-on to leaf-off [2] & & 1.76 & & 2.58 & \\ \hline \multirow{2}{*}{MLS} & forward to backward [3] & 0.86 & & 0.84 & 0.54 & 0.77 \\  & leaf-on to leaf-off [2] & & 2.13 & & 3.57 & \\ \hline \multicolumn{2}{c}{TLS leaf-off to MLS leaf-off [2]} & 3.28 & & 3.21 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracies of the MLS and TLS co-registration. [1] plane-to-plane distance; [2] ICP with 3D point-to-point distance; [3] 3D point-to-point distances.  
the stem. The missed three trees were detected by manual inspection afterwards, and used to model and compare the tree height, CPAs and QSMs. In plot 2, all trees were detected (no omission error) with 49 overdetected trees. All commission errors in plot 2 originated from the large number of small understory trees.
## 2 Materials and Methods  
The LCA methodology quantifies the environmental impacts of a process or a product through a systematic set of procedures. This technique compiles and examines the inputs and outputs of materials and energy, which results in the quantification of the environmental impacts directly associated with the operation of processes or products throughout its life cycle [10]. Results from an LCA can be used to inform the public sector, stakeholders and manufacturers about potential environmental impacts from products and processes and suggest possible improvements [11].  
The EoL of PV technologies is not commonly included in LCA studies due to the lack of information and data on the subject. However, there are a few studies, particularly for c-Si, that focus on the impacts of recycling processes for PV technologies [6; 7] and some of them even compare these process with other scenarios such as landfill [8] and incineration [9]. Although the mentioned works are a step in the right direction, there is a need for a more comprehensive inventory of different recycling possibilities. For example, it is still challenging to understand the impacts of landfilling these devices and the possible benefits of recycling and recovering materials from used modules and cells. With this in mind, here we present an LCA to compare the effects of different EoL scenarios for c-Si solar modules, including recycling processes. The functional unit to be used in this analysis is defined as 1 kg of silicon-based PV waste modules.  
This study assumed common c-Si PV module materials: silicon wafer cells with silver-based and aluminium contacts, ethylene vinyl acetate (EVA) encapsulant, aluminium frame, polymer (Tedlar(r)) back-sheet, cover glass, tinned copper tabbing and lead-containing solder. The assessed impacts due to transportation of modules from their collection site to their EoL process (landfill, incineration or recycling plant) depend on a set of assumptions. It has been shown in a previous article [7] that, for a recycling plant 400 km from the collection point, using a truck for transportation adds important environmental impacts to the recycling process, particularly, for abiotic depletion. However, the results were calculated for a specific transport and location. In this LCA, a separate analysis on the transportation phase is presented because the impacts do not depend on the recycling technology but on the collection system and distance. The assessment of the environmental impacts was performed using the LCA methodology (based on the ISO standard [12]) with the aid of GaBi software, version 6 [13]. The calculations are based on data collected from the literature and the Ecoinvent database, version 2 [14]. For a better understanding of the LCA results, ReCiPe (2016) endpoint indicators are used for expressing the environmental impacts [15].
## 3 Enhanced Mathematical Model with Atmospheric and Environmental Effects
### Enhanced Two-Ray Model with Atmospheric and Rain Attenuation  
For the hybrid model, we extend the two-ray model with atmospheric and rain attenuation, suitable for long-range, high-altitude propagation in 5G-satellite communication.  
Given a ground station height \(h_{\text{G,t}}\), satellite height \(h_{\text{S,t}}\), and distances \(r_{1,t}\) and \(r_{2,t}\):  
\[r_{1,t}=\sqrt{(d_{1,t}+d_{2,t})^{2}+(h_{\text{S,t}}-h_{\text{G,t}})^{2}} \tag{3}\]  
\[r_{2,t}=\sqrt{(d_{1,t}+d_{2,t})^{2}+(h_{\text{S,t}}+h_{\text{G,t}})^{2}} \tag{4}\]  
Including atmospheric and rain attenuation, the received power for each path is  
\[P_{r,1}=P_{t}\bigg{(}\frac{\lambda}{4\pi r_{1,t}}\bigg{)}^{2}e^{-(A_{\text{ atm}}+\gamma_{r})\cdot r_{1,t}} \tag{5}\]  
\[P_{r,2}=P_{t}\bigg{(}\frac{\lambda}{4\pi r_{2,t}}\bigg{)}^{2}\Gamma e^{-(A_{ \text{atm}}+\gamma_{r})\cdot r_{2,t}} \tag{6}\]  
where \(\Gamma\) is the reflection coefficient based on surface characteristics. The total received power \(P_{r}\) is  
\[P_{r}=|E_{r,1}+E_{r,2}\mathrm{e}^{j\Delta\phi}|^{2}=P_{r,1}+|\Gamma|^{2}P_{r,2 }+2\sqrt{P_{r,1}P_{r,2}}\cos(\Delta\phi) \tag{7}\]  
Here, \(\Delta\phi\) is the phase difference, which accounts for Doppler shifts and other delays.
# Smart Poly(acrylic acid)/Poly(acrylamide) Microgels with Interpenetrating Polymer Network Structure
## 5 Conclusions  
PAA/PAAM MGs with an IPN structure were synthesized and their response to temperature and pH was studied. In contrast to the previously reported so far \"straight\" PAAM/PAA IPN nanogels, where the order of the network formation starts with PAAM followed by PAA formation, here the \"reverse\" PAA/PAAM IPN MGs were obtained. The synthesized IPN MGs show pH and temperature responsiveness, as revealed by DLS and diffusion NMR spectroscopy. A phase-separated structure was observed for some of the IPN compositions, which was similar to their bulk analogues and was due to the \"reverse\" order of the IPNs' formation. Depending on their composition and when close to a 1:1 stoichiometric ratio, the IPN MGs have a very compact, spherical shape close to a hard sphere, in contrast to the neat PAA and PAAM MGs which possess a more open and heterogeneous shape typical for polymer coils. The results of this study indicate that PAA/PAAM IPN MGs have good potential for drug delivery and also show a smart response to pH and temperature. Testing these novel materials in real drug delivery applications is planned.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app142411562/s1](https://www.mdpi.com/article/10.3390/app142411562/s1)]([https://www.mdpi.com/article/10.3390/app142411562/s1](https://www.mdpi.com/article/10.3390/app142411562/s1)), Scheme S1. Structural formulas of poly(acrylic acid) (A) and polyacrylamide (B). Scheme S2. Synthesis of PAA/PAAM IPN MGs via inverse miniemulsion method. Figure S1. Relative increase of the Rh of IPN MGs as a function of pH (compared to their initial radius at pH 2.6) calculated by the Rh data obtained by DLS, for pH \(\leq\) 5. Figure S2. \({}^{1}\)H NMR spectra of PAAM and PAAM MG at three different pH values. Figure S3. \({}^{13}\)C NMR spectra of PAAM and PAAM MG at three different pH values (only the characteristic region for CONH\({}_{2}\) (COOH) groups is presented). Figure S4. AF4 fractograms for samples PC0 (A), PC25 (B) and PC100 (C) molar masses (symbols), LS signal (solid line), RI signal (broken line) and vs. elution time. Table S1. Regression analysis data for the relative increase of the Rh of IPN MGs, for pH \(\leq\) 5.  
Conceptualization: M.S. and E.V.; methodology: M.S. and P.S.; AF4 analysis and interpretation S.B. and A.L.; data curation: M.S. and P.S.; writing: M.S., S.B. and E.V.; original draft preparation: M.S. and E.V.; project administration: E.V. All authors have read and agreed to the published version of the manuscript.  
This research was funded by DAAD, Grant number DNTS Germany 01/12.  
Not applicable.  
Dataset is available on request from the authors.  
Research equipment of Distributed Research Infrastructure INFRAMAT, part of Bulgarian National Roadmap for Research Infrastructures, supported by Bulgarian Ministry of Education and Science was used in this investigation.  
The authors declare no conflicts of interest.
# Energy Consumption Prediction for Fused Deposition Modelling 3D Printing Using Machine Learning
## 6 Discussion  
The benefits of artificial intelligence and machine learning algorithms in 3D printing are wide-ranging. In our case, before each printing, we will predict the printing energy depending on the part's orientation. Figure 4 illustrates a graphical comparison between the actual energy values and the predicted energy for each part and according to its different orientations using the GPR model. It is clear that the two curves perform in a similar manner, so we can use the GPR model to find the best part orientation for printing, which will use less energy.  
To assess the performance of our approach, four parts from the database were selected at random. For each part (Table 4), we will predict the energy needed for printing according to the three orientations (0, 90, and 180).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Part** & **Orientation** & **Predicted Energy by GPR** & **Actual Energy** \\ \hline \multirow{4}{*}{\(\begin{bmatrix}\text{\bf 1}\\ \text{\bf 1}\end{bmatrix}\)} & **0** & **70.01837754** & **76.969** \\  & 90 & 97.62380459 & 92.866 \\  & 180 & 196.11750589 & 143.278 \\ \hline \multirow{4}{*}{\(\begin{bmatrix}\text{\bf 1}\\ \text{\bf 1}\end{bmatrix}\)} & 0 & **44.3041344** & **90.218** \\  & 90 & 158.10571579 & 138.686 \\  & 180 & 166.36283187 & 133.051 \\ \hline \multirow{4}{*}{\(\begin{bmatrix}\text{\bf 1}\\ \text{\bf 1}\end{bmatrix}\)} & 0 & **78.20556709** & **89.38** \\  & 90 & 185.35942611 & 96.941 \\ \cline{1-1}  & 180 & 98.38320959 & 90.133 \\ \hline \multirow{4}{*}{\(\begin{bmatrix}\text{\bf 1}\\ \text{\bf 1}\end{bmatrix}\)} & 0 & **77.05352922** & **67.718** \\  & 90 & 203.28819197 & 141.432 \\ \cline{1-1}  & 180 & 86.72962282 & 75.973 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Examples of comparison between actual energy and predicted energy by GPR.  
Figure 4: Actual versus predicted values by GPR for each part.  
From the results shown in Table 4:  
* For part number one, if we print it following orientation 180 instead of printing it following 0, we will lose about 70 Wh of energy.
* For part number two, if we print it following orientation 90 instead of printing it following 0, we will lose about 50 Wh of energy.
* For part number three, if we print it following orientation 90 instead of printing it following 0, we will lose about 7 Wh of energy.
* For part number four, if we print it following orientation 90 instead of printing it following 0, we will lose about 90 Wh of energy.  
These pertinent results confirm the capacity of our model based on the GPR algorithm to predict energy consumption in the FDM process with high accuracy.
# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 4 Experimental Setup
### Benchmark Methods
#### 4.5.1 Least Square  
The LS algorithm is often used for channel estimation. This method aims to minimize the square of the error between received pilots and transmitted pilots.
# Dynamic SDN Controller Load Balancing
## 3 Network Architecture  
### Dynamic Cluster Flow Architecture  
In the architecture we presented in [25; 26], we considered always only one controller called Super Controller (SC) that is connected to all other controllers in the control plane. In this sense, we considered a two-tier hierarchy where the SC is in the top tier and all other controllers are in the lower tier. The SC gathers load information from all controllers and is responsible for grouping the controllers into clusters according to their load. For each cluster, the SC handles a Cluster Vector (CV), which includes the addresses of all the controllers inside the given cluster. The CV provides each controller inside a cluster the ability to identify to which controller it can transfer part of its overload. In order for each controller to intelligently decide where to transfer its load, an efficient communication scheme between the controllers is necessary. This is because each overloaded controller that requires the help of another controller becomes dependent on the other controller which itself can be also overloaded, etc. This whole interdependency requires a complete and synchronized coordination between all the controllers.
## 6 Underwater Object Localization Service Case Study Relying on Multi-Sensor Data Fusion
### Technology Model  
The topology and the tasks to be carried out in each of the network's nodes are described in the technology model. We concentrate on the technological implementations of the InformA function from the application layer because of its extensive design. An extract from the extensive model (Figure 14) is showcased. To carry out the InformA application function, a wide range of technology functions such as SendTo are connected at the technology layer. A message of type SIP or Diameter can be forwarded or sent from one node to another via the SendTo function.  
In the context of technology, we have extended the ArchiMO tool in [21; 26] by utilizing the IMS meta-model and ArchiMate. IMS standards are mostly considered and contained in this extension [54]. After the simulation program is executed, the NS-3 simulator creates an animation script. The NetAnim tool imports this script to display the simulation scenario's animation (see to Section 6.4, which depicts the messages that are transmitted and received between the various nodes. The transparency of our model transformation methods is demonstrated by the produced topology and by watching the message exchanges between the various nodes.
# Capacity Analysis of Lattice Reduction Aided Equalizers for Massive MIMO Systems
## 1 Introduction
This document provides an assessment of the effectiveness of the lattice reduction algorithms for enhancing the capacity of the massive MIMO system. The main objective of this paper is to present the capabilities of the KZ aided linear receiver to improve the capacity of a massive MIMO system. In particular, the main contributions of this paper are the following:  
* This study provides a mathematical calculation to find the capacity gain of the MMSE receiver over the ZF receiver. * It highlights the impact of the channel correlation on the capacity. * An expression to established the capacity gain due to lattice reduction-aided receivers over the linear counterpart is derived. * The lattice reduction-aided receivers are shown to perform much better than the conventional linear receiver in terms of capacity gain in a massive MIMO system. It shows that the lattice reduction-aided receivers perform much better than LLL-enabled IF receiver. * The achievable capacity corresponding to LLL- and KZ-based lattice reduction-aided receivers are compared, establishing that KZ-aided receivers perform much better. The rest of this paper is organized as follows. Section 2 provides a literature survey about related work. Brief descriptions about some receiver systems such as MF-, ZF-, MMSE-, IF-, and LLR-aided receivers are presented in Section 3. After that, we provide an overview of the Nakagami-m channel with its probability distribution function and statistical behavior in Section 4. Section 5 represents the mathematical calculation to show the impact of the channel correlation over the capacity of a MIMOsystem. In addition, this section presents the capacity improvement due to the use of LLR-aided receivers and provides a comparative study between LLL and KZ lattice reduction. All the analytical results are included in Section 6. Finally, we summarize the paper in Section 7.
## 3 Experimental Results
### Efficiency Comparison  
The computation time is calculated to evaluate the efficiency of TongueNet. The comparison of TongueNet and REF is displayed in Table 5. To reflect the computation time, we calculated the average time consumed per image after applying TongueNet and REF. The results show that TongueNet requires less than 0.3 s compared to REF, which used 0.302s. The training time of TongueNet is 2435 s.
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 5 Description of the Scenarios for FANET Test
### Scenario 1--Baud Rate of 115,200 bps  
This scenario simulates the situation where a boat is found by a FANET node. There is no failure of any node. In order to validate the network operation, 500 packets of 256 bytes were sent. This procedure was repeated 72 times with the aim to calculate the mean and standard deviation of the samples. Packet sending time, packet loss, signal strength (RSSI) and throughput were obtained.  
In this configuration shown in Figure 13, the BS and the network coordinator were on the ground. The first UAV was added to the network as a router (HUB), flying at a height of 80 m from the ground. Its respective position carried from 400 m to 600 m away from the BS. The second UAV was added as an end device, flying at a height of 80 m from the ground, with the position varying between 900 m and 1100 m from the BS. The baud rate for all XBee modules was set at 115,200 bps as listed in Table 5.  
Figure 12: Test site and position of UAVs.  
Figure 13: ZigBee devices in a scenario 1 with mesh configuration.
# SINDVis: User-Centered Dynamic Interactive Visualization System for Space Information Networks
## 3 Theory and Methods
### SINDVis Description of Functions
#### 3.2.1 Descriptions of GeoView Functions  
Based on the inspiration of the STK [15], and in order to complement the TopolView, we developed a web-oriented visual view, GeoView. This view is not only able to implement the features that STK contains, but also partially expands. The specific functions are as follows.  
* **Function 1**: GeoView can realistically simulate and demonstrate the composition and operational status of SIN, and it can achieve a stereoscopic display (3D) and planar display (2D). Meanwhile, the view also can display the relevant geospatial environment.
* **Function 2**: GeoView can show the specific network structure and connection relations of the GEO layer satellite system and the MEO/LEO layer satellite system. It can show the orbit and coverage of the satellite systems. In addition, GeoView can display the group style, running mode, and coverage range of unmanned aircrafts (UAs) and the hot air balloons (HABs) in the Near Space layer information system. Meanwhile, it can also show the specific locations of Ground layer terminals and their connections with other information systems.
* **Function 3**: GeoView can determine the parameters of visual elements by setting a hot zone, and demonstrating the dynamic transmission of data information among different links.
* **Function 4**: GeoView can enable freely switching of 3D, 2.5D, and 2D information, and it can also switch multiple map backgrounds and operating environments. Meanwhile, it can realize the real-time synchronization of data information. Users can zoom in and zoom out of the views freely based on individual needs. This means that the overall view information can be displayed as well as the detailed view information, and the whole process will not cause the distortion of the view.  
According to the users' needs, we initially set the functions of GeoView in the entity domain presented above. Wherein, the functions may have repetition. In order to express different goals, and we carried out the elaboration separately. Of course, there are many more GeoView functions, and they can be developed and expanded according to the needs of users.