esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Studies that account for these variables offer a more nuanced interpretation of implant success and provide more individualized patient care guidelines. For instance, the influence of smoking and periodontal disease history on bone stability around implants has been documented, with conflicting reports on their impact on bone loss [43; 45]. The interaction between these covariates and the type of implant and study design can complicate the interpretation of results. For example, while [PERSON] et al. [42] found no significant difference in bone loss between narrow- and regular-diameter implants, the patient's oral hygiene and compliance, which were not explicitly discussed in the study, could have a substantial impact on these outcomes. Similarly, the retrospective nature of studies such as those by [PERSON] et al. [38]and [PERSON] et al. [49] can introduce recall bias and selective reporting, which might confound the results. This systematic review presents some limitations. In the comparative analysis of MBL, the homogeneity of study groups is another factor that influences the reliability and comparability of results. Studies like that of [PERSON] et al. [46] demonstrate high homogeneity by using multi-center data with standardized protocols, enhancing the comparability of their findings. Conversely, studies with heterogeneous groups, such as the case-control study by [PERSON] et al. [44], require careful interpretation, as the variability within the sample could mask or exaggerate the true effect of the implant characteristics on bone loss. Another limitation is the lack of data that allow us to classify the results in subgroups to identify the factors influencing bone loss. In future studies, it would be interesting to analyze bone loss according to the measurement method used to observe the differences.
## 6 Conclusions  
This study investigated the impact of dividing the networks in WLAN by introducing virtual LAN and how this could improve the performance of networks in terms of metrics such as delay, throughput and PDR. Riverbed academic Edition v17.5 was used as a simulation tool for our investigation. This study consisted of 15 testable scenarios in two cases for a low-density network and a high-density network. Two important traffics (File Transfer FTP, Web browsing HTTP and Email) were applied in all scenarios. The use of the VLAN improved the network by reducing the delay and data dropped of the wireless network; however, the throughput was reduced, which is a major drawback of VLANs. Therefore, this throughput could be improved using the routing protocols examined in this study. The aim of this paper is to improve the VLAN for the wireless network by utilizing three ad hoc routing protocols such as AODV, DSR and OLSR for low and high traffic density in a number of different scenarios, using Riverbed Modeler Simulation program in terms of the number of quality-of-service parameters. The results showed that ad hoc routing protocols improve the delay, throughput and traffic dropped performance of the VLAN, as shown in Figure 7. As the number of workstations was increased, AODV achieved higher improvement in throughput for low density and high density, but its delay was increased when the number of workstations was increased. OLSR achieved better delay improvement, even when the traffic increased, as shown in Figure 8. Traffic dropped was also an improvement in this network performance for the low-density and high-density cases, as shown in Figures 9 and 10, respectively. The utilization of these routing protocols reduced the traffic dropped in the VLAN, which represented an extra improvement to the VLAN network in wireless communication so that the better selection of the ad hoc routing protocol played an important role in delay or throughput improvement. In this paper, the three ad hoc routing protocols also improved the performance of the Wireless  
Figure 11: Delay, Throughput and traffic dropped of WSN. (**a**) Delay of WSN. (**b**) Throughput of WSN. (**c**) Traffic dropped of WSN.  
Sensor Network in terms of delay, throughput and traffic dropped, as shown in Figure 11. Consequently, this study recommends the use of a smart device to determine the type of routing used in terms of the delay or throughput requirements.  
Conceptualization, S.W.N. and Y.A.M.; methodology, H.A.A.; writing--original draft, Y.A.M. and H.A.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data is available as required.  
The authors declare no conflict of interest.
## 5 Experiments
### Experiment 1--Reconstruction of Acorn
 \\ \hline \multirow{4}{*}{2} & BPA & 2.6412 & 2.19942 & 3.43703 & 11.6783 & 2.16232 & \(-\)48 & \(-\)60 \\  & PowerCrust & 9.50837 & 6.57579 & 11.5607 & 23.4187 & 9.52517 & \(-\)55 & \(-\)81 \\  & Poisson & 2.74878 & 2.24832 & 3.55114 & 11.1559 & 2.15936 & 99 & \(-\)35 \\  & VSR & 0.710704 & 0.709221 & 1.00403 & 3.40886 & 0.477692 & \(-\)12 & \(-\)4 \\ \hline \multirow{4}{*}{3} & BPA & 1.65938 & 1.34632 & 2.13684 & 6.20571 & 1.36133 & \(-\)27 & \(-\)35 \\  & PowerCrust & 1.68578 & 1.57828 & 2.30927 & 6.71023 & 2.13402 & \(-\)12 & \(-\)32 \\  & Poisson & 2.13187 & 1.64198 & 2.69809 & 7.89814 & 1.73299 & 73 & \(-\)77 \\  & VSR & 0.926695 & 1.01773 & 1.37641 & 4.35121 & 0.537287 & \(-\)19 & \(-\)17 \\ \hline \multirow{4}{*}{4} & BPA & 1.41748 & 1.34832 & 1.95631 & 6.20571 & 1.02828 & \(-\)22 & \(-\)24 \\  & PowerCrust & 1.51101 & 1.66481 & 2.24826 & 6.71748 & 0.778636 & \(-\)17 & \(-\)26 \\  & Poisson & 2.59704 & 2.0811 & 3.32798 & 9.51066 & 2.05565 & 45 & \(-\)6 \\  & VSR & 0.876429 & 1.12105 & 1.42297 & 4.59235 & 0.350886 & \(-\)18 & \(-\)15 \\ \hline \multirow{4}{*}{5} & BPA & 0.88351 & 0.79243 & 1.19042 & 4.05483 & 0.658741 & \(-\)11 & \(-\)4 \\  & PowerCrust & 0.639664 & 0.67545 & 0.930259 & 3.68787 & 0.394742 & \(-\)9 \\  & Poisson & 1.96806 & 1.64165 & 2.6285 & 2.70361 & 1.4991 & 67 & 30 \\  & VSR & 0.411081 & 0.476312 & 0.629169 & 3.02812 & 0.227498 & \(-\)8 & \(-\)1 \\ \hline \multirow{4}{*}{6} & BPA & 0.865362 & 0.801298 & 1.17937 & 4.05483 & 0.608282 & \(-\)10 & \(-\)5 \\  & PowerCrust & 0.656822 & 0.805945 & 1.03968 & 4.19336 & 0.321317 & 1 & \(-\)13 \\ \cline{1-1}  & Poisson & 1.91001 & 1.51941 & 2.44062 & 7.74737 & 1.53498 & 79 & 43 \\ \cline{1-1}  & VSR & 0.456283 & 0.632145 & 0.779608 & 3.41585 & 0.191891 &* For the other three methods, the quality of the reconstructed surfaces drops dramatically as the number of contours decreases (as seen for two or three contours in Figures 4 and 5). However, as the number of contours decreases, both the quality and the Hausdorff distance for the VSR method show little variation. Even with only two contours, the VSR surface reconstruction closely approximates the original surface. * Visually and quantitatively, the Poisson method performs the worst, except when reconstructed with two contours. Although Poisson and VSR are implicit-function-based methods, the Poisson method fails to reconstruct a satisfactory surface in all cases. * Although the Power Crust method claims to be capable of producing watertight surfaces, it fails to do so when it is performed on two contours. This makes it the worst method for surface reconstruction with only two contours. * As an intuitive and efficient method, BPA succeeds in constructing a triangle mesh for all cases. When the input data are dense (_i.e._, seven contours), it can produce a satisfactory surface of the acorn. However, its performance decreases dramatically as the number of contours decreases.
# Compatible Ground Motion Models for South Korea Using Moderate Earthquakes
## 2 Materials and Methods
### Comparisons  
Relative residuals, which are essentially z-scores, were calculated to assess the differences between observed recordings and GMM predictions using the following equation:  
\[Z_{i}=\frac{\ln SA_{Observed,i}-\ln PSA_{GMPE,i}}{\sigma_{ln,GMPE,i}} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **GMM** & **Magnitude Range (M\({}_{W}\))** & **Distance Range (km)** & **V\({}_{\text{SS0}}\) Range (m/s)** \\ \hline \multirow{8}{*}{NGA-West.} & AS08 [2] & 5.0–8.5 & 0–200 & N/A \\  & BA08 [1] & 5.0–8.0 & 0–200 & 180–1300 \\  & CB08 [3] & 4.0–8.5 \({}^{\text{A}}\) & 0–200 & 150–1500 \\  & CY08 [4] & 4.0–8.5 \({}^{\text{A}}\) & 0–200 & 150–1500 \\  & I08 [5] & 5.0–8.5 \({}^{\text{A}}\) & 0–200 & \textgreater{}450 \\ \hline \multirow{8}{*}{NGA-West 2} & ASK14 [6] & 3.0–8.5 & 0–300 & 180–1500 \\  & BSSA14 [7] & 3.0–8.5 \({}^{\text{A}}\) & 0–400 & 150–1500 \\  & CB14 [8] & 3.3–8.5 \({}^{\text{A}}\) & 0–300 & 150–1500 \\  & CY14 [9] & 3.5–8.5 \({}^{\text{A}}\) & 0–300 & 180–1500 \\  & II4 [10] & 5.0–8.0 & 0–150 & 450–2000 \\ \hline \multirow{8}{*}{NGA-East} & DASG15-ICC [11] & & & \\  & DASG15-1 CV [11] & & & \\ \cline{1-1}  & DASG15-2 CC [11] & & & \\ \cline{1-1}  & DASG15-2 CV [11] & & & \\ \cline{1-1}  & PZCT18 [12] & 4.0–8.0 & \textless{}1000 & 3000 \\ \hline \hline \end{tabular}  
* N/A not available, not an input parameter, or not clearly stated. \({}^{\text{A}}\) dependent on faulting mechanism. \({}^{\text{B}}\) distance is treated as closest distance to surface projection of fault rupture plane.  
\end{table}
Table 4: The NGA GMMs used for comparison analysis.  
where \(i\) is the station ground motion recording as an index identifier, \(SA_{Observed,i}\) is the acceleration response spectral ordinate from Rot50 for the \(i\)th recording station, \(PSA_{GMM,i}\) is the PSA from one of the aforementioned NGA GMMs for conditions at the \(i\)th recording station, and \(\sigma_{ln,GMM,i}\) is the corresponding standard deviation given in the GMM description in natural log units for the \(i\)th recording station conditions. Although this formulation might seem strange and can show some bias, this was implemented because the standard deviation is in natural log units and allows for a better interpretation of how compatible the GMMs are given the available data. This approach was also used in a European study [26].  
With respect to determining which GMMs would be a good fit, a summation of the squares is taken of the relative residual:  
\[Score=\frac{1}{n}{\sum_{i}^{n}}(Z_{i})^{2} \tag{2}\]  
where \(n\) is the number of recordings per earthquake. This makes \(Score\) a metric per earthquake. In this case, the lower these metrics are, the better the GMM compatibility with moderate South Korean earthquakes.
# A Long Short-Term Memory Network-Based Radio Resource Management for 5G Network
## 5 Conclusions  
In this paper, an LSTM-based RRM is proposed to allocate the optimum power and bandwidth to the desired UE based on the data rate. Here, the hyperparameters of the LSTM are identified using GSO, which further improves the performance of RRM. The request queue is considered in the 5G system to accomplish the priority-based resource allocation for the desired UEs. The priority-based RRM minimizes the traffic and avoids the unwanted resource allocation through the 5G environment. Moreover, the burst error and ISI in the 5G environment are minimized by using frequency interleaving and guard interval insertion. The LSTM-RRM method performs the power allocation in both the macro and micro cells of 5G environment. The performance of the LSTM-RRM method is improved in terms of throughput, outage percentages, and dual connectivity when compared to the DRRM, QOC-RRM, or EPAS. The indoor guaranteed rate of LSTM-RRM for a building distance of 1400 m is improved up to 75.38% when compared to the QOC-RRM.  
The paper investigation, resources, data curation, writing--original draft preparation, writing--review and editing, and visualization were done by [PERSON] and S.K. The paper conceptualization, software, validation, and formal analysis were done by [PERSON] and H.K.L. Methodology, supervision, project administration, and final approval of the version to be published were conducted by [PERSON]-[PERSON] and P.B.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Calibrating Microscopic Traffic Simulation Model Using Connected Vehicle Data and Genetic Algorithm
## 3 Methods
### Data Preparation  
As mentioned above, the presented architecture requires two datasets as input for the VISSIM simulation model: the O/D matrix, which contains all types of vehicle origins and destinations within the network, and connected vehicles' trajectory data. To create the O/D matrix, a traffic detection method should be applied within the network. The number of vehicles moving through the network should be countable using this way. If the connected vehicle dataset achieves one hundred percent market penetration, there is no need to use any other data collection method. However, such a comprehensive dataset is currently unavailable in most traffic networks. In this study, RTMS are used to collect historical traffic volume data (passenger car equivalent) within the network. Installing multiple sensors at  
Figure 2: Detailed client-side process within the calibration framework, as referenced in Figure 1.  
various locations within the network facilitates the creation of an O/D matrix easily using simple calculations. Both the Wejo dataset and RTMS dataset are available upon request.  
The connected vehicles' trajectories are extracted from the Wejo dataset. The Wejo dataset is a collection of connected vehicle data that captures detailed trajectories and behavioral information of vehicles on real-world roads. In this dataset, each vehicle is assigned a unique Journey Identification (JID). For each JID, information, such as longitude and latitude, speed, and time-stamps, is recorded every second. The Wejo dataset must first be filtered for the road network under consideration because it includes data on connected vehicles traveling all roads. Using the geofencing method, all vehicles outside the designated network are filtered out. In the geofencing method, some polygons around the area of interest are defined. Any GPS coordinates outside of these polygons are excluded from the dataset. By completing this process, the noise in the data is reduced, and only relevant trajectories are included. Moreover, the dataset needs to be filtered for the specific project period.  
Having cleaned the dataset, a script is designed to prepare the datasets for input into the VISSIM simulation model. This script first examines all connected vehicle trajectories within the network during the simulation period to capture vehicle origins, destinations, and entry times into the network. It then creates a list of this information, making it ready for the main script to place the vehicles in the simulation at the exact times and locations corresponding to the real world and assigns route decisions to the vehicles based on their destinations. All the vehicles created in the simulation using this method are categorized as connected vehicles. Meanwhile, all the vehicles created in the simulation using the O/D matrix derived from the RTMS data are categorized as non-connected vehicles. To refine the data, all vehicles from the Wejo dataset are subtracted from the RTMS dataset based on their time-stamps and locations.
## 3 Motivation and Challenges
With the expected emergence of quantum computers, current Blockchain networks would become vulnerable and need to transition to methods using quantum-resistant cryptography and quantum networks [53; 54; 55]. Current blockchain relies on cryptographic algorithms for Key-pair generation such as RSA and Elliptic-curve cryptography. The Key pair consists of a private key used by its owner to sign the transactions he issues, and the public serves to verify the digital signature by the rest of the network. Cryptographic algorithms are one-way functions, which means that current computers cannot be used to derive the private key from its matched public key. However, using a quantum computer, any current cryptographic algorithms can be broken. The generation of RSA cryptographic key pairs can be broken using [PERSON]'s quantum computing algorithm [56]. Quantum computers are still at an early stage of research, a technology that is expected to be operational over the next decade [57]. In the meantime, researchers are working to develop quantum cryptography, since the current one would not be relevant in the presence of quantum computers. This cryptography which is immunued against quantum computing is called Post-Quantum Cryptography PQC. In the context of blockchain, PQC involves the development of a quantum-resistant cryptographic signature. PQC is intended to be safe against quantum computers while being implementable on a conventional computer. Many proposals have been submitted to the National Institute of Standards and Technology NIST. However, after 3 rounds of preselection, only three finalist post quantum cryptographic digital signature algorithms remain, with one only to be selected as the new standard [58]. The three finalist are CRYSTALS-Dilithium [59], Falcon [60] and Rainbow [61].
## 4 Discussion  
Trace-driven simulations have revealed that our approach shows adaptability to the complexity of the particular context of Live-Streaming in vCDN with respect to the state-of-art algorithms designed for general-case SFC deployment. In particular, our simulation test revealed decisive QoS performance improvements in terms of acceptance ratio with respect to any other _backtracking_ algorithm. Our agent progressively learns to adapt to the complex environment conditions like different user cluster traffic patterns, different channel popularities, unitary resource provision costs, VNF instantiation times, etc.  
We assess the algorithm's performance in a bounded-resource scenario aiming to build a safe-exploration strategy that enables the market entry of new vCDN players. Our experiments have shown that the proposed algorithm, E2-D4 QN is the only one to adapt to such conditions, maintaining an acceptance ratio above the general case state-of-art techniques while keeping a delicate balance between network throughput and operational costs.  
Based on the results in the previous section, we now argue the main reasons that make E2-D4 QN the most suitable algorithm for online optimization of SFC Deployment on a Livevideo delivery vCDN scenario. The main reason for our proposed algorithm's advantage is the combination of the enhanced exploration with a dense-rewards mechanism on a dueling DDQN framework. We argue that such a combination leads to discover convenient long-term actions in contrast to convenient short-term actions during training.
## 4 Experiments and Results
### Experimental Setting  
We use accuracy, recall, F1-score, and precision as the performance evaluation metrics for the study in this paper, which are based on the four essential elements of the Confusion Matrix (CM): true examples (TP, True Positives), false-positive examples (FP, False Positives), true-negative examples (TN, True Negatives), and false-negatives (FN, False Negatives).  
The classification accuracy (\(Acc\)) to evaluate the quality of predicted labels is defined as follows:  
\[Acc=\frac{1}{|M|}\sum_{x_{i}\in M}1\big{\{}y_{i}^{\prime}=y_{i}^{true}\big{\}} \tag{29}\]  
where \(M\) is the set of samples in the testing set, \(|M|\) is the cardinality of the set, and \(y_{i}^{\prime}\) and \(y_{i}^{true}\) denote the predicted label and the accurate label of sample \(x_{i}\), respectively. A 10-fold crossover validation is adopted to reduce the random training/testing set partition effect.  
Recall, F1-score, and precision metrics are defined below:  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{30}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Number of Instances** & **Number of Attributes** & **Number of Class** \\ \hline AsianReligionsData & 590 & 8265 & 8 \\ Arrhythmia & 279 & 452 & 16 \\ CNAE & 1080 & 856 & 9 \\ Gait & 48 & 321 & 16 \\ Ad & 3279 & 1558 & 2 \\ Parkinson & 756 & 754 & 2 \\ QSAR & 1687 & 1024 & 2 \\ QSAR\_oral & 8992 & 1024 & 2 \\ REJAFADA & 1996 & 6826 & 2 \\ SCADI & 70 & 206 & 7 \\ Secom & 1567 & 591 & 2 \\ Urban & 168 & 148 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the four datasets from the UCI machine learning repository.  
\[\text{precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{31}\]  
\[\text{F1}-\text{score}=2*\frac{\text{precision}*\text{Recall}}{\text{precision}+ \text{Recall}} \tag{32}\]  
In the following experiments, we first perform parameter settings to analyze the performance of the baseline model on high-dimensional data. Second, a sensitivity analysis is performed for the attribute screening factor \(\beta\) proposed in Section 3 to visualize and analyze the performance of the feature space with different attribute screening factor constraints. Subsequently, ablation experiments are set up to explore the contributions of the feature selection and classifier integration components and visualize the changes in feature space reconstruction. Finally, the performance of the HDELC strategy proposed in this paper is compared with other ensemble learning methods and advanced integration techniques for high-dimensional data based on all the datasets in Table 1.
# EMD-Shannon Entropy-Based Methodology to Detect Incipient Damages in a Truss Structure
###### Abstract  
Truss-type designs are widely used in civil structures. Despite the fact that they are robust and reliable structures, different kinds of damage can appear. In order to avoid human and economic losses, the development and application of damage-detection methodologies are paramount. In this work, a methodology based on the empirical mode decomposition (EMD) method and the Shannon Entropy Index (SEI) to detect incipient damages associated with corrosion in a 3D 9-bay truss-type bridge is presented. As different EMD methods are presented in literature, the most representative methods are investigated in order to evaluate their performance for this task. To this end, the vibration signals generated in the truss-type bridge at different conditions are analyzed. For the damage condition, four severity levels of simulated corrosion (1 mm, 3 mm, 5 mm, and 8 mm of diameter reduction) generated into the elements of truss-type bridge are considered. Results demonstrate the effectiveness of the proposal in terms of detecting corrosion in its very early stage (1 mm of reduction in the element).  
structural health monitoring; vibrations; empirical mode decomposition; truss structure; Shannon entropy; incipient damage detection; corrosion Article EMD-Shannon Entropy-Based Methodology to Detect Incipient Damages in a Truss Structure