esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Evaluation
### SI Cancellation by Adaptive Filter
#### 4.3.2 EVM Evaluation  
To precisely quantify the enhancement by AF-based SI cancellation on the RX UL signal, we introduce the EVM method as a measure of assessing constellation points. The fundamental concept of EVM is illustrated in Figure 14. Essentially, the EVM method quantifies how much the signal deviates from its expected position, providing a quantitative measure of digital signal processing performance.  
For instance, in Figure 14, the TX signal constellation is represented by the red cross marked with the code '11'. The cumulative distribution function (CDF) curves of the RX signals are presented in Figure 15. In this figure, the red and blue curves correspond to results obtained with and without AF-based SI cancellation, respectively. Furthermore, the solid, dashed, and dotted curves are examples depicting signal performance at SNRs of 10, 15, 20, and 25 dB.  
Analysis of the CDF results in Figure 15 reveals that at the 50% CDF point, which roughly signifies the average position within a distribution, the EVM assessment indicates an improvement of approximately 30 dB in signal constellations when employing AF-based SI cancellation.  
Figure 14: Definition of EVM evaluation.  
Figure 13: Constellation after AF processing.
## 1 Introduction
Moreover, the displacement transmissibility of the two isolators is evaluated and compared with that of other corresponding linear isolators. (2) The shock isolation performances of both isolators are evaluated using three indices of shock responses under base excitations of half-cycle sine pulses. Meanwhile, their results are also compared with those of other corresponding linear isolators. It should be noted that the sensitive microscopy instrumentation and precision measuring instruments used for micro-level research require an ultrastable operating environment, one as free as possible of ambient and operational vibrations. These vibrations can be caused by wind, seismic activity, nearby construction and transportation, elevators, pumps, compressors, fans, motors, and air handling equipment appearing both outside and inside a building. Most of their excitation frequencies are in the ultralow-frequency or low-frequency range (about 0.01 Hz-20 Hz). To achieve effective vibration isolation in this situation, the passive vibration-isolated system used for instruments mentioned above necessitates a lower natural frequency than what has been previously available with traditional techniques. Interestingly, the proposed isolators in this paper provide a novel approach for satisfying the demand of the natural frequency. The rest of this paper is arranged as follows: in Sections 2 and 3, we firstly discuss the direct parallel connection of the inerter and the negative stiffness element incorporated into the conventional isolator. Secondly, the series connection of the inerter with the viscous damper, which, together, is then connected with the negative stiffness element in parallel is investigated in Section 4. Finally, some conclusions are drawn in Section 5.
# Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization
## 4 Proposed Method
### Iacs
#### 4.2.1 Duplication Cleaning  
In this step, we compare the intersection and complement sets of each neighbor node with each other to find any duplication set. If the duplication set exists between the neighboring nodes, only the node with the shortest distance to the blind node is selected to avoid delay when connecting to far-away nodes. Therefore, less communication between the nodes is needed for the localization process when the duplicated data are ignored.  
For example, Figure 3 shows the neighbor set of the blind node is {0, 1, 6, 7, 8, 9} and the neighbor set for node Id = 0 is {1, 2, 3, 5, 7, 8, 9}. The intersection set is the elements of the neighbor node's  
Figure 4: Two main steps in IACS method to remove the redundant neighboring node.  
Figure 3: Example scenario of a blind node surrounded by a set of neighboring nodes. Each node has its own neighbor set. The arrows show the distance in meters (\(m\)) between the neighboring nodes and the blind node.  
neighbor set that intersects with the blind node's neighbor set. Therefore, the intersection set of blind node and node Id = 0 is selected as [1; 7; 8; 9]. The complement set on the other hand is the elements of the neighbor node's neighbor set that do not intersect with the blind node's neighbor set, which result in the selection of [2; 3; 5]. Based on Figure 3, Table 5 is tabulated. Table 5 shows that the complement set of nodes 1 and 7 are duplicated. For this case, node 7 is removed as the distance of node 7 to the blind node is further than the distance of node 1 to the blind node. Later, node 9 is removed due to duplication of the intersection set with node 8 and it has a further distance to the blind node than node 8.
## 3 Proposed Methodology
### Brief Description of OLSR  
The OLSR protocol is a table-driven routing protocol specifically developed for MANETs. It simplifies routing logic and offers high efficiency. Nodes in OLSR transmit Hello messages to their 1-hop neighbors, electing a set of MPRs, as illustrated in Figure 1. Only MPRs forward topological information and create link-state information. OLSR is especially effective in large and dense networks. However, selecting the minimum set of MPRs poses challenges, making it necessary to explore algorithms that optimize this selection process. Table 1 presents the structure of a standard Hello message used in the Optimized Link State Routing (OLSR) protocol within a Mobile Ad Hoc Network (MANET).  
\begin{table}
\begin{tabular}{c c c} \hline \hline Reserved & Htime & Willingness \\ \hline Link code & Reserved & Link Message Size \\ \hline \hline \end{tabular}
\end{table}
Table 1: Standard Hello message in OLSR Protocol.  
Figure 1: Multipoint relays illustration.  
Through Hello messages, the OLSR protocol defines 2-hop neighbors and accomplishes a distributed election of a set of MPRs. When a node is selected as an MPR, there is always a path to each of its 2-hop neighbors. OLSR offers the best paths in terms of the number of hops and is particularly suitable for large and dense networks. The OLSR routing protocol presents several restrictions regarding the calculation of the minimum MPRs (NP-complete problem). Hence, defining a node's MPRs is an interesting problem, and some algorithms should be used to find the optimal result.
## 4 Discussions and Conclusions  
In this work, we suggested FC-coupled license plate with a beam-steering-technique-based V2V-VLC system. The feature of FC was tested and confirmed that there are advantages, such as high gain and wide FoV. Off-the-shelf white WLED headlamps and a blue LD channel to the FC-coupled license plate were evaluated with DCO-OFDM scheme, under various practical illuminances. As a solution to tackle situations such as LOS blockage and misalignment, the beam-steering and tracking system using the image processing and the steerable mirror was employed. The maximum data rates achieved in this demonstration were 54 Mbps and 532 Mbps from the WLED headlamps and from the blue LD, respectively. In particular, the blue LD was able to configure a beam-steering and tracking system with \(\pm 25^{\circ}\). In addition, a real-time black-box video streaming through the beam-steering channel was demonstrated.  
Future work includes constructing a bi-directional V2V-VLC link using the modulated taillights through optical camera communications, the investigation on the dynamic outdoor environment, including the signal crosstalk caused by the modulated beams from adjacent vehicles, developing a multi-vehicle tracking and selection mechanism. Further optomechanical optimization will also be performed for practical and compact form factor.  
Conceptualization, H.C., S.O., S.C. and M.Y.; methodology, H.C. and S.O.; software, S.O., Y.L., M.Y., S.C. and S.J.; validation, H.C., S.O. and Y.L.; formal analysis, H.C. and S.O.; investigation, S.O., Y.L., M.Y. and S.C.; resources, H.C.; data curation, S.O. and Y.L.; writing--original draft preparation, S.O. and H.C.; writing--review and editing, H.C, S.O., Y.L., M.Y., S.J. and S.C.; project administration, H.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Incheon National University Research Grant in 2019.  
Not applicable.  
The data presented in this study are available on request from the corresponding author and on his personal website.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Introduction to IPv6
### Transition Methods  
Transition methods support gradually moving from IPv4 to IPv6 without major interruptions of services and networks. It is expected that most networks will have to support both IPv4 and IPv6 in parallel for a long time because of legacy equipment and the dependency on others to completely switch to the new protocol. Transition methods can be categorized into tunneling and translation methods. Figure 2 shows a hierarchical representation of the transition methods.
## 4 Results
### Experiments and Analysis  
#### 4.2.1 Ablation Experiments and Parameter Selection  
The experimental environment is configured with Python3.11, PyTorch2.0.1, Intel(R) Core(TM) i9-12900H CPU, NVIDIA GeForce RTX3060 GPU, and 6 GB RAM. During training, we used the Adam optimizer [43] for 50 epochs, with the initial learning rate set to 0.0001. The batch size is set to 128 when training the network. The properties of the dataset used for the experiments are described in Section 2. During data preprocessing, the short-time Fourier transform (STFT) is applied with a window size of 128, using a Hamming window and a hop size of 20.  
In the proposed TEResNet, the number of channels in the first convolutional layer and the basic block serves as a crucial parameter. It directly impacts detection accuracy while also playing a significant role in determining the degree of the lightweighting of the model. To investigate the effect of this parameter, we conducted four sets of experiments, denoted as parameter 1, 2, 3, and 4, where M, N, P, and Q are used to denote the number of channels in the first convolutional layer and the three basic blocks; the specific settings are shown in Table 3. The loss function curves and model performance for the four sets of parameters are shown in Figure 13. As the number of channels increases, the number of model parameters gradually increases, and the loss function decreases faster. However, parameter 4, with the fastest decreasing loss function, does not lead to the best test accuracy, which may be due to overfitting caused by the larger model parameters. Therefore, we chose the model with parameter 3 for continuous signal testing and comparison experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **M** & **N** & **P** & **Q** \\ \hline Parameter 1 & 8 & 8 & 8 & 16 \\ Parameter 2 & 8 & 8 & 16 & 24 \\ Parameter 3 & 8 & 16 & 24 & 32 \\ Parameter 4 & 8 & 24 & 32 & 48 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Four sets of parameters, corresponding to the number of channels in the first convolutional layer and the three subsequent basic blocks.  
In order to explore the impact of the dilated convolution, we conducted ablation experiments; the loss and accuracy curves for conditions using and not using the dilated convolution of parameter 3 are given in Figure 14. As can be seen from the figure, the loss of the model using the dilated convolution decreases faster and the test accuracy is higher, verifying that the dilated convolution is able to expand the sensory field and thus improve the model accuracy.
## 2 Space Debris--A Primer
### What Can We Do About This Problem?  
When confronted with the task of cleaning up the near-orbit environment of all the debris that is in it, there are a number of options and factors to consider. These factors might include, for example, the feasibility of the effort (e.g., cleaning up debris in GEO is a different challenge than cleaning up debris in LEO) and time criticality (e.g., a large and object that will re-enter shortly and has been designed to disintegrate upon re-entry versus a small one with an exceedingly long orbital lifetime versus one that poses a ground risk because it will likely not disintegrate upon re-entry).  
An early discussion [13] of various debris removal technologies and their effectiveness shows that when choosing between (1) cleaning up small debris particles or (2) cleaning up large spent rocket boosters and large satellites, while the former might be easier than the latter, the right thing to do is, in fact, the latter and not the former. Simply put, to effectively reduce the debris population, we need to bring down all the big objects that are up there first, then, as time and resources allow, address the smaller objects. After all, if you spend all your time and money chasing down and cleaning up millions of little pieces of junk, all it takes is a single collision between two large boosters to immediately put all those millions of small pieces back in orbit. Ref. [14] provides an overview of various more recent approaches and technologies under consideration and actually developed by a number of space agencies and private industries to clean up the space environment.  
If debris population growth is unchecked or not sufficiently controlled, there is the very real possibility of the Kessler Syndrome. In this scenario, a runaway or chain reaction of impacts between debris pieces and satellites or other debris pieces in earth orbit would create a permanent and opaque cloud of debris dust [15]. This would, in turn, render some regions of Earth orbit unusable, thereby threatening the sustainability of space activities for many generations to come. This possibility underscores the need for all countries to engage in coordinated activities of space traffic management and reduction in debris generation to enable continuing space operations in Earth orbit.  
Figure 5: Re-entry of a spent satellite, including landfall uncertainties (Image Credit: The Aerospace Corporation).
## 6 Experiments and Discussion
### Field Experiments
#### 6.3.2 Navigation Reliability  
To evaluate the methods more effectively, 3D positioning error was calculated and statistically analyzed. The 3D positioning differences between the test schemes and the reference are shown in Figure 13. The positioning errors of STC4 and STC5 are much larger than those of others. CNE-TC performs better than STC4, STC5 and ATC. But when the environment becomes challenging, CNE-TC cannot cope with the large measurement errors effectively. MATC provides a smaller positioning error due to the RMNCE based satellite selection. RMNCE-TC performs best owing to not only the satellite selection algorithm but also the adaptive UKF. Figure 14 shows the expanding scales calculated by Equation (28) and we adopted the maximum as the final value at each epoch.  
In practical applications, a position accuracy within 2 m is an important criterion for evaluating the reliability of a navigation solution [5]. Figure 15 shows the corresponding 3D error histograms. The percentages within 2 m are 38.79%, 50.48%, 57.35%, 80.36%, 91.23% and 76.88% for STC4, STC5, ATC, MATC, RMNCE-TC and CNE-TC respectively.  
Figure 14: The value of the expanding scale \(\beta_{i}\) with respect to the five pseudo-range measurements: (a) 1150 s to 1250 s; (b) 5070 s to 5120 s.  
Figure 13: 3D positioning differences of the test schemes with respect to the reference trajectory.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **STC4** & **STC5** & **ATC** & **MATC** & **RMNCE-TC** & **CNE-TC** \\ \hline Latitude (m) & 1.8205 & 1.7114 & 1.3430 & 1.1557 & 0.5697 & 1.5629 \\ Longitude (m) & 2.2633 & 2.1034 & 1.7706 & 1.4231 & 0.8689 & 1.1514 \\ East velocity (m/s) & 0.1124 & 0.1078 & 0.0595 & 0.0301 & 0.0299 & 0.0300 \\ North velocity (m/s) & 0.1122 & 0.1337 & 0.0406 & 0.0382 & 0.0370 & 0.0475 \\ Heading (\({}^{\circ}\)) & 0.9092 & 0.8389 & 0.6072 & 0.6101 & 0.6061 & 0.6001 \\ Pitch (\({}^{\circ}\)) & 0.2838 & 0.2537 & 0.1925 & 0.1905 & 0.1835 & 0.1921 \\ Roll (\({}^{\circ}\)) & 0.1957 & 0.1832 & 0.1852 & 0.1860 & 0.1852 & 0.1858 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Global RMSE values of the different schemes.
# A3C-R: A QoS-Oriented Energy-Saving Routing Algorithm for Software-Defined Networks
## 5 Experimental Evaluation
### Experimental Environment and Comparison Algorithms
(3) DDPG-EEFS, an energy-saving flow scheduling mechanism based on DRL algorithm [22]. This algorithm optimizes network energy consumption using deep reinforcement learning. Given that A3C-R also aims to reduce energy consumption, comparing it with DDPG-EEFS allows us to assess A3C-R's energy-saving performance relative to other energy-efficient routing strategies. The comparison content includes QoS performance indicators such as network delay, throughput, and packet loss rate, as well as network energy-saving effects. All comparison experiments were conducted under the same experimental conditions, including network topology, traffic patterns, and configuration settings, to ensure a fair and consistent evaluation across all algorithms.
# Design and Implementation of Virtual Security Function Based on Multiple Enclaves
## 4 System Design
### System Structure
#### 4.1.2 Untrusted Part  
The untrusted part includes system call library, socket module, DPDK, and log module. Among them, log module is responsible for recording the log information during the processing of the data packet. The DPDK is primarily responsible for reading the encrypted packets from the NICs queue and put into untrusted memory by polling. The system call library includes multiple ecalls and ocalls functions, which are mainly responsible for the conversion between the trusted part and the untrusted part of the system. The socket module is primarily responsible for communication.
## 4 Experimental Results and Discussion
### Evaluation of the Objective Color Correction Network
#### 4.2.4 Comparison of Different Methods  
Subjective results of the compared methods are shown in Figure 9. From left to right, they are the uncorrected images, results of the polynomial-based correction method, the SVR-based correction method, the K-PLSR-based color correction method, and our method. Our algorithm significantly outperforms other methods in subjective quality.  
To evaluate the performance of the four compared algorithms under five different illuminances, the mean chromatic distances \(\Delta_{Emean}\)) and the maximum (\(\Delta_{Emax}\) are shown in Table 8. We can see that the mean of color differences our PCC performs best among the four compared algorithms. Compared with the maxima of the color differences, the result of our OCC network is more stable, which can get better results in practice.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Uncorrected** & **POLY** & **SVR** & **K-PLSR** & **OURS** \\ \hline \(\Delta E_{mean}\) & 5.03 & 4.01 & 2.17 & 1.95 & 1.32 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison results of the mean color differences between two devices.  
Figure 8: Four color correction methods applied on facial and tongue images and their corrected results, upper: deceive 01, bottom device 02. From left to right, they are uncorrected and the results of the POLY, SVR, K-PLSR, and Ours.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Device** & \(\Delta E_{ab}^{*}\) & **Uncorrected** & **POLY** & **SVR** & **K-PLSR** & **OURS** \\ \hline \multirow{2}{*}{001} & \(\Delta E_{mean}\) & 12.07 & 9.15 & 4.22 & 2.88 & 2.23 \\  & \(\Delta E_{max}\) & 26.89 & 21.00 & 13.82 & 7.71 & 7.87 \\ \hline \multirow{2}{*}{002} & \(\Delta E_{mean}\) & 18.24 & 9.23 & 2.64 & 3.55 & 2.18 \\  & \(\Delta E_{max}\) & 47.46 & 35.09 & 17.37 & 10.73 & 8.41 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison results of the color differences using four methods for two devices.
# A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization
## 3 Methodology  
### Single-Diode PV Modeling  
A PV module consists of a number of PV cells connected in series to increase the voltage output and a number of PV cells connected in parallel to increase the current output. PV modules can generate electricity autonomously or combine with more PV modules in series or in parallel to form a PV array that generates a greater amount of electricity. The equivalent circuit of a PV module can be observed in Figure 1.  
The output current of a PV module can be calculated by the following calculation using [PERSON]'s first law [21]:  
\[I=N_{p}I_{ph}-N_{p}I_{d}-I_{p}, \tag{1}\]  
where \(I\) is the output current of the PV module, \(N_{p}\) is the number of cells connected in parallel, \(I_{ph}\) is the photogenerated current, \(I_{p}\) is the current flowing in the parallel resistance of the PV module \(R_{p}\), and \(I_{d}\) is the diode's current, as described by [PERSON] [22]:  
\[I_{d}=I_{s}\left[e^{\frac{V_{d}}{2N_{d}}}-1\right], \tag{2}\]  
where \(I_{s}\) is the diode's saturation current, \(V_{d}\) is the diode's voltage, \(n\) is the diode's ideality factor, and \(V_{t}\) is the diode's thermal voltage, which is calculated as follows:  
\[V_{t}=\frac{kTN_{s}}{q_{c}}, \tag{3}\]  
where \(k\) is the Boltzmann constant [23], \(T\) (in Kelvin) is the diode's temperature, \(N_{s}\) is the number of PV cells connected in series, and \(q_{c}\) is the elementary charge. Combining (1) with (2) and (3), the output current of the photovoltaic module is:  
\[I=N_{p}I_{ph}-N_{p}I_{s}\left[e^{\frac{(R_{p}(V+I_{0})}{2 kTN_{s}}}-1\right]- \frac{V+IR_{s}}{R_{p}}, \tag{4}\]  
where \(V\) is the output voltage of the PV module and \(R_{s}\) is the series resistance of the PV module.  
Combining more than one PV module, a PV array can be formed, which constitutes the electrical power generator unit of a PV power station. The way that PV modules are connected defines the output voltage and the output current of the PV array. If there are \(n\) PV modules connected in series and \(m\) PV modules connected in parallel, then the  
Figure 1: PV module equivalent circuit.  
output voltage and the output current of the PV array are given, respectively, by the following equations.  
\[V_{array}=nV, \tag{5}\]  
\[I_{array}=mI, \tag{6}\]  
where V and I are the output voltage and current, respectively, of a single PV module. The number of parameters used in this model are summarized in Table 1.
## 2. Mature Technologies for Water and Wastewater Remediation
### Ultraviolet Radiation
#### 2.3.2 UV Radiation Process  
When UV radiation (frequently of 245 nm wavelength) is applied to contaminated water, the radiation penetrates microorganisms in the water and alters their DNA (Deoxyribonucleic Acid), rendering them unable to survive and incapable of reproduction. This puts the pathogen cells out of action, thereby effecting the decontamination requirement.