esa-sceva/satcom-chunk-collection huggingface dataset:
# A Micro-Segmentation Method Based on VLAN-VxLAN Mapping Technology
## 2 Relation Work
### YxLAN  
The use of Virtual Local Area Networks (VLAN) for network isolation has been standard practice in traditional networks. However, the VLAN technology has its limitations and can no longer fulfill the requirements for tenant and service isolation in cloud computing environments, as the maximum number of subnets it supports is 4094.  
An important advantage of cloud computing data centres is their high service availability. Achieving this level of availability requires virtual machines to be migrated without interruption across three layers of devices in the data centre, regardless of their physical location. Moreover, it is necessary that both IP and MAC addresses remain unchanged during the migration process. This is in contrast to traditional networks, which are unable to maintain the same IP address after traversing three layers of devices.  
VxLAN technology presents itself as a better solution to the previously mentioned problems. Its 24-bit Virtual Network Identifiers (VNIs) allow for over 16 million segments, meeting the requirements of tenant and service isolation in cloud and other large datacentres or complex environments. Furthermore, VxLAN employs MAC-in-UDP tunneling technology to create layer 2 tunnels within the data centre, which enable virtual machines to roam the data centre without changes to their IP and MAC addresses. As a result, VxLAN technology was widely used in cloud data centre scenarios.  
[PERSON], [PERSON] et al. [25] presented a VxLAN-based in-band network telemetry method that enables high-precision monitoring of data centre networks. The article [26] proposed a load-balancing approach based on VxLAN tunneling technology to improve performance, reduce latency, and avoid congestion. The work presented in [27] introduces an optimisation scheme for VxLAN based on the software-defined networking (SDN) paradigm. The proposed approach aims to improve network performance by reducing latency, increasing throughput, decreasing packet loss, and enhancing load balancing and network utilisation. These articles separately presented the application scenarios and benefits of VxLAN technology within cloud data centres, without its application in micro-segmentation scenarios.  
The article [28] proposed a traffic control approach that improves high availability and traffic distribution performance using vxlan technology instead of stp technology. The article employed a direct one-to-one mapping approach between VLAN and VxLAN, primarily focusing on the implementation of data migration within the cloud. This approach served as a valuable inspiration for our subsequent research.
## 3 Results and Discussion
### Analysis of ANOVA  
The Taguchi method helped to find out the optimal number of experiments and provides a way to optimize the system through _S/N_ ratios. For the further solidification of the results obtained in the Taguchi method based on _S/N_ ratios, an ANOVA statistical model is used. Unlike the Taguchi method it calculates the percentage contribution of each factor involves. Tables 5 and 6 shows the ANOVA table for the _PE_ and \(T\). The significance of control factors in ANOVA is determined by comparing the F values of each control factor. These tables were calculated with a 5% significance level and a 95% confidence level. If the _p_-value for the factor is greater than 0.05, then it will be considered as a non-significant factor. Therefore, based on the  
The percentage contribution in the ANOVA tables shows the magnitude of the influence of the parameters calculated by Equation (8). The contribution of the gripper torque, tool speed, force, and feed rate for maximizing the polishing efficiency are 3.93%, 19.96%, 71.56%, and 0.57%, respectively. Similarly, for the polishing torque minimization, these percentages are 0.66%, 3.01%, 90.54%, and 1.12%. The most significant parameter for both responses is the normal contact force (71.56% for \(PE\), and 90.54% for \(T\)). The percent of error was considerably low at 4.12% and 4.66% for PE and T respectively. Table 7 shows the cell mean of the control factors in ANOVA for \(PE\) and \(T\). For polishing efficiency, the best input parameter are \(T_{g}\) = 0.767 (level 1), \(\omega\) = 0.802 (level 3), \(F_{z}\) = 0.864 (level 3), and \(V_{f}\) = 0.751 (level 1). Similarly for the \(T\), the best input parameters are \(T_{g}\) = 0.368 (level 1), \(\omega\) = 0.350 (level 1), \(F_{z}\) = 0.243 (level 1), and \(V_{f}\) = 0.366 (level 3). Results from the ANOVA are well matched with the Taguchi method results for the best input parameters.
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 3 System Model
### Proposed Scheme-II (Key Recovery System Using Proxy Re-Encryption)
#### 3.3.3 Security Requirements of Proposed Scheme-II  
The security requirements of the key recovery system using proxy re-encryption are as follows:  
* KRF integrity: No participant in key recovery can maliciously transform \(KRF\) information from the device and \(KRF\) information required for key recovery cannot be changed.
* Data confidentiality: It should be possible for only authorized devices to decrypt encrypted data.
* Med applied for support: The session key used for communication must be encrypted and stored in \(KRF\). In the event of an emergency when it is necessary to view the device's data, the encrypted session key must be able to recover the encrypted message according to the procedure determined by \(Med\) as needed.
* Collusion attack resistance: Fewer than three participants among the \(Med\), \(KRC\), and \(KRA\) should not be allowed to obtain keys even if they are maliciously colluding.
* Key escrow problem: \(KGC\) can generate private keys for all participants, but the complete private key must not be known.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 4 Discussion
### Statistical Analysis  
Throughout the presented systematic literature review, a total of 126 scientific papers have been analysed and evaluated, out of which, 67 (53%) are journal articles, 33 (26%) appear in proceedings, and 24 (19%) are surveys/review papers, covering a range of 30 years, i.e., from 1992 to 2022, as shown in Figure 18. As expected, there appears to be an increasing interest from the research community in wireless positioning technologies over the last decade, and especially since 2018, with more than 10 publications per year.  
The reviewed literature is further analysed in Figure 19, with respect to (a) the localization technique and (b) the wireless technology employed. Out of the 126 scientific papers, it was observed that the most common localization technique was fingerprinting with 17.46%. In addition, range-based techniques (i.e., lateration and angulation) were used in 19.05% of the reviewed papers, while range-free localization techniques only appeared in 10.32%. Moreover, time-based measurements were the preferred method for position estimation, with 23.81%, followed by angle-based measurements with 19.05% and channel-based with RSS and CSI receiving 16.67% and 8.73%, respectively.  
Figure 18: Year of publication for the reviewed literature, of which 67 (53%) are journals, 33 (26%) are proceedings, and 24 (19%) are surveys.  
Regarding the wireless technologies, Wi-Fi was the most popular, with 16.67%, followed by GNSS/Indoor and IMU, both with 11.90%, whereas LoRa was the least popular, with 2.38%. A matching between the different localization techniques and wireless technologies in the related literature is shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**Wireless Technologies**} \\ \hline
**Techniques \({}^{\star}\)** & **
## 2 Methodology
### Methodology for Calculating Fatigue Damage
#### 2.2.1. Blade Characteristics and Geometry
In the current case, the two mentioned regimes exist, but a third \(-\)hybrid \(-\) regime was introduced, where a constant rotor speed coexists with a zero pitch angle. This \"stall-regulation\"-type behavior was achieved by deviating from optimal operation at a relatively low wind speed (8 m/s), well below the turbine design wind speed of 11 m/s, where the nominal power was attained. It should be noted that the pitching curve \(\beta(v)\) showed a gentle almost monotonic increase from zero to about six degrees at 20 m/s. * Pitch-regulated wind turbine with variable rpm at nominal power: This scheme was introduced for comparative purposes and represented a hybrid version between the former two approaches. As shown in Figure 5b, in this scheme, the rotor speed was varied in a fashion identical to the one used for stall regulation and a corresponding adjustment in the pitch angle curve (Figure 5d). Though there did not seem to be an objective, practical reason for using this strategy, using the same rotor speed curve is a useful tool for assessing the effect of the rotor thrust on the rotor dynamics, as simulated by FAST, and, consequently, on the fatigue loading and damage. Figure 5: Operation curves of the WTs’ designs: power curve, rotational speed, torque curves, and pitch angle. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Stall Regulation** & **Pitch Regulation** &
\begin{tabular}{c} **Variable Speed Pitch Regulation** \\ **Regulation** \\ \end{tabular} \\ \hline Nominal power & 10 kW & 10 kW & 10 kW \\ \(V_{\text{cut-in}}\), \(V_{d}\), \(V_{\text{cut-out}}\) & 3 m/s–11 m/s-20 m/s & 3 m/s-11 m/s-20 m/s & 3 m/s–11 m/s-20 m/s \\ Rotational speed & Variable & Nominal: 130 rpm & Variable \\ Pitch angle (\(\beta\)) & 0 & Variable & Variable \\ Variable losses & 13\% & 13\% & 13\% \\ \(\lambda_{opt}\) & 6.1 & 6.21 & 6.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Operational characteristics of the three wind turbine designs studied in this work.
###### Abstract  
The purpose of this study was to determine tissue concentration of toxic metals, namely Mn, Cr, Ni, Cu, Fe, Cd, Pb, and Zn, in the removed third molars in patients from the Legnica-Glogow copper district. A group of 69 patients with an average age of 27.3 \(\pm\) 6.9 years was enrolled into the study. There were 16 (23.2%) men and 53 (76.8%) women. Patients were divided into two groups according to the place of residence--residents of the Legnica-Glogow Copper Area (n = 49) and the control group, residents of Wroclaw (_n_ = 20). Determination of the Pb, Cd, Cr, Ni, Fe, Mn, Cu, Zn content was performed by atomic absorption spectrometry in an air-acetylene flame using the SpectraAA atomic absorption spectrometer with a V2 AA240 PS flame attachment. The content of Fe and Pb in the third molars was higher among residents of the Legnica-Glogow Copper Area (_p_ = 0.016 and \(p\) = 0.002, respectively). The significant risk factors that may contribute to the accumulation of toxic metals in teeth were identified. We showed a significant correlation between the level of Cr, Cu, and Zn and age, and between chromium and vitamin D3 concentration in the blood (_p_ < 0.05 for all).  
toxic metals; third molars; vitamin D3; biomonitoring; elements
# A Fully-Automatic Gap Filling Approach for Motion Capture Trajectories
## 3 Proposed Method  
This section introduces the automatic gap filling algorithm. The methods are framed in three subsections.  
Section 3.1 (initialization) describes the structures that are defined a priori to enable the frame-wise gap filling routine. Unlike other approaches in the literature that rely on the definition of rigid-bodies, this step performs an automatic search of the set of markerswith the potential to assist the reconstruction of the other markers, taking the entire set of markers in that trajectory as a possibility. This is a novel approach that addresses a major limitation of other skeleton-based methods that typically require prior knowledge about skeleton constraints.  
The next subsection (Section 3.2) details the frame-wise gap filling routine. This is a two stage approach: first, we define the set of markers that will be used in a specific frame to reconstruct all markers that are missing in that frame; then, we estimate the coordinates of these markers by defining a set of equations that shall be simultaneously minimized using least squares minimization.  
Finally, in Section 3.3, we introduce a mechanism of multiple initialization of the algorithm at different frames of a trajectory that is conceived to promote gains in both performance and efficiency.  
The algorithm is designed to fill gaps in a fully labelled MoCap sequence; thus, correctly labeling the markers is a requirement of the algorithm.
## 5 Conclusions  
This paper introduces a centralized single OLT to support many users in a WDM ring architecture-based OMAN. A traditional OLT of the OMAN is replaced with a cost-effective OMC source that offers numerous frequency tones to carry the bidirectional information across different spans of fibers. These fibers are connected in a ring architecture with different RNs, and ten ONUs can be connected to each RN based on the star topology. At each RN, part of the downlink signal, which can be considered colorless, is utilized for uplink transmission. The OMC source generates more than sixty comb lines with measurable parameters, making it more suitable to deploy in an OMAN that is meant for entertaining many subscribers while facilitating heavy data rate support. Across each carrier of the generated OFC, 20 Gbps data are transmitted based on optical duobinary modulation. At the ONU side, VOAs are utilized to calculate the BERs of the received signal, which ranges from \(10^{-3}\) to \(10^{-13}\) for the downlink transmission. Similarly, an OOK is utilized for the uplink transmission to test the feasibility of the proposed OFC in the OMAN system. The BER during the uplink transmission ranges from \(10^{-6}\) to \(10^{-14}\). Furthermore, the detailed analyses are discussed and explained with their respective results for the various cases of main optical fiber failures in the optical metro access network, along with the system's robust switching mechanism.  
Methodology, R.U., S.U., A.A.A.-A. and J.R.; Software, R.U., A.A.A.-A., J.R. and S.C.; Validation, R.U., S.U. and S.C.; Formal analysis, A.A. and H.S.A.; Investigation, A.A. and H.S.A.; Resources, H.S.A. and J.R.; Data curation, R.U., S.U. and S.C.; Writing--original draft, S.U., H.S.A. and S.C.; Writing--review & editing, A.A. and A.A.A.-A. All authors have read and agreed to the published version of the manuscript.  
National Key Research and Development Program of China (No. 2018 YFB1801703); National Natural Science Foundation of China (No. 61835005, 61822507, 61522501, 61475024, 61675004, 61705107, 61727817, 61775098, 61720106015, 61875248); Beijing Young Talent (No. 2016000026833 ZK15); BUPT Excellent Ph.D. Students Foundation (No. CX2020301); Open Fund of State Key Laboratory of Information Photonics and Optical Communications (Beijing University of Posts and Telecommunications); Jiangsu talent of innovation and entrepreneurship; Jiangsu team of innovation and entrepreneurship; Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. SJKY19_0972).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.
###### Abstract  
The highway-rail grade crossings (HRGCs) crash frequency models used in the US are based on the Federal Railroad Administration's (FRA) database for highway-rail crossing inventory. Inaccuracies or missing values within this database directly impact the estimated parameters of the crash models and subsequent crash predictions. Utilizing a set of 560 HRGCs in Nebraska, this research demonstrates variations in crash predictions estimated by the FRA's 2020 Accident Prediction (AP) model under two scenarios: firstly, employing the unchanged, original FRA HRGCs inventory dataset as the input, and secondly, utilizing a field-validated inventory dataset for the same 560 HRGCs as input to the FRA's 2020 Accident Prediction (AP) model. The findings indicated a significant statistical disparity in the predictions made with the two input datasets. Furthermore, two new Zero-inflated Negative Binomial (ZINB) models were estimated by employing 5-year reported HRGCs crashes and the two inventory datasets for the 560 HRGCs. These models facilitated the comparison of model parameter estimates and estimated marginal values. The results indicated that errors and missing values in the original FRA HRGCs inventory dataset resulted in crash predictions that statistically differed from those made using the more accurate and complete (validated in the field) HRGCs inventory dataset. Furthermore, the crash prediction model estimated upon the corrected inventory data demonstrated enhanced prediction performance, as measured by the statistical fitness criteria. The findings emphasize the importance of collecting complete and accurate inventory data when developing HRGCs crash frequency models. This will enhance models' precision, improve their predictive capabilities to aid in better resource allocation, and ultimately reduce HRGCs crashes.  
Keywords:rail-highway intersections; grade crossing inventory data; data validation; transportation safety data; generalized linear models +
Footnote †: journal: Applied
# A Joint Channel Estimation and Compression Method Based on GAN in 6G Communication Systems
## 2 System Model and Problem Formulation
### System Model  
Consider the channel estimation scenario in the orthogonal frequency division multiplexing (OFDM) system over the frequency selective fast fading channel, and the structure of an OFDM block with \(N_{s}\) symbols in the time domain and \(N_{f}\) subcarriers in the frequency domain is depicted in Figure 1. In the case of frequency selective fast fading, the channel environment keeps constant within each element during transmission but varies among elements. Then the received OFDM symbols \(y(i,j)\) at the receiver can be expressed as  
\[y(i,j)=h(i,j)s(i,j)+w(i,j),\ 1\leq i\leq N_{s},\ 1\leq j\leq N_{f}, \tag{1}\]  
where \(s(i,j)\) denotes the transmitted symbols at the \(i\)-th symbol and \(j\)-th subcarrier, \(h(i,j)\) denotes the channel coefficient of the wireless link between transceiver and receiver, and \(w(i,j)\) is the additive white Gaussian noise with 0 mean and \(\sigma^{2}\) variance.  
As shown in Figure 1, an OFDM block contains two types of elements, named pilot element and data element, respectively. In particular, prior known symbols are transmitted in the pilot elements to obtain the partial instantaneous channel state of the entire OFDM block, while the data symbols are delivered in the data elements. Define \(\alpha_{p}(i,j)\) as the  
Figure 1: The structure of an OFDM block.  
position indicator of pilot elements, \(\alpha_{p}(i,j)=0\) or 1. Specifically, if \(\alpha_{p}(i,j)=1\), it indicates that the OFDM element located at \(i\)-th symbol and \(j\)-th subcarrier is used to transmit pilot symbols; otherwise, it means that the OFDM element belongs to the data element set. Then \(y(i,j)\) in (1) can be divided into two parts, which are the received pilot symbols \(y_{p}(i,j)\) and data symbols \(y_{d}(i,j)\). Their expressions can be written as:  
\[y_{p}(i,j) =\alpha_{p}(i,j)y(i,j), \tag{2a}\] \[y_{d}(i,j) =(1-\alpha_{p}(i,j))y(i,j). \tag{2b}\]  
Similarly, \(h(i,j)\), \(s(i,j)\) and \(w(i,j)\) can also be divided into the pilot and data terms with the same form of (2a) and (2b), respectively.
# Dual aptamer-Functionalized 3D Plasmonic Metamolecule for Thrombin Sensing
## 3 Results
Notably, the change of the CD signal was already noticeable at thrombin concentrations as low as 100 pM. The intensity then steadily increased up to 10 nM where it did not plateau but instead fell again (Figure 2a, red arrow with 1 \(\upmu\)M thrombin) as soon as the concentration of the thrombin rose above the K\({}_{\text{d}}\) values of the individual aptamers (20.2 nM and 3.5 nM for TBA and HD22, respectively). [20] We inferred that above these concentrations, two thrombin molecules could separately bind to each aptamer and thereby could unlock the structure. Up to these concentrations, the normalized response showed typical sigmoidal behavior and we were able to fit the data points up to 10 nM, with a Hill equation yielding a dissociation constant (K\({}_{\text{d}}\)) of 1.4 nM and a Hill coefficient of 1.3 \(\pm\) 0.3 (cf. Table S1). As this hill coefficient was similar to that of the engineered multimeric ligands' [38], we concluded that the reaction showed moderate cooperativity from multiple binding sites as expected from the working principle of our metamolecule. To further confirm this, we also analyzed our metamolecule with the thrombin aptamers in the mirrored positions (Figure S3). Indeed, this version of the metamolecule showed the same hill coefficient (1.3 \(\pm\) 0.1). Finally, to confirm that our metamolecule specifically only detected thrombin, we added 100 nM of bovine serum albumin (BSA) to the metamolecules, which led to no response from our sensor (Figure S4).
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## Abbreviations  
* Anti-Resonant Fiber
* Anti-Resonant Reflecting Optical Waveguide
* Bit Error Ratio
* Capital Expenses
* Chromatic Dispersion
* Confinement Loss
* Differential Group Delay
* Double Nested Anti-Resonant Nodeless Fiber
## 3 Machine Learning
### Role of Machine Learning in Cybersecurity Applications for Apt Detection  
Nowadays, massive and targeted attacks are more frequent. These attacks can cause damage to users or organisations such as the loss of sensitive information. Researchers are studying different approaches to prevent or minimise the risk of attacks. Some of the methods and techniques that researchers have used are directly related to machine learning.  
The prevention measures require higher capacity for analysis and response in the shortest possible time, due to the large volume of data and the rapid evolution of current threats. For this reason, automated tools have been created to assist cybersecurity administrators. Machine learning techniques are a useful tool in the field of cybersecurity. For example, models of network traffic behaviour can be created to detect anomalous activity, reduce the number of false positives on alarms, and detect threats in real-time [49]. However, machine learning can be used to create attacks, for example, on sending fraudulent emails or password cracking software [50]. Machine learning applications in cybersecurity can be classified as follows [51]:  
* Detection: These are the tools that allow the detection of abnormal behaviour to generate alerts in real-time, and to facilitate decision-making.
* Protection: Detect vulnerabilities to install security fixes automatically.
* Prediction: Techniques and algorithms to predict attacks and develop anti-malware techniques.
* Termination: Automatically eliminating the threat.  
Machine learning techniques applied to cybersecurity can help system administrators find unusual behaviour in the network an organisation, for example, an APT.  
Some keys approaches to detecting APT are: (1) Observe unusual alerting patterns to detect malware with malicious load recognition, known components, and remote control activities. (2) Monitoring suspicious outbound traffic on the network can display significant parameters such as infected computers, C&C centres, and data exfiltration. (3) Monitoring unexpected internal traffic on the network could reveal escalating privileges, lateral movements, and malware propagation. Some of these cybersecurity applications using ML techniques are described below.