esa-sceva/satcom-chunk-collection huggingface dataset:
### Leveraging Network Functions Virtualization (NFV) in the Design of IIoT-Edge Networks  
According to the ETSI, Network Function Virtualization (NFV) transforms network services that are traditionally implemented as hardware-based appliances [105] into Virtual Network Functions (VNF) deployed as Virtual Machines (VMs) or containers. NFV tackles the challenge of hardware-based appliances nearing their end of life due to accelerated technology and innovation [105; 106]. Hardware-based appliances such as firewalls, load balancers, Virtual Private Network (VPN) gateways, Deep Packet Inspection (DPI), Session Border Controllers (SBC), carrier-grade Network Address Translator (NAT), Quality of Experience (QoE) monitor, and Wide Area Network (WAN) acceleration are hosted on standard industrial server hardware [105; 107]. There are several benefits of adopting ETSI NFV in the IIoT-Edge network:  
* **Cost Reduction:** Capital and operational expenses are reduced with reduced hardware-based appliance deployment and reduced power consumption.
* **Scalability and Flexibility:** It facilitates dynamic scaling of VNF VMs or container instances to meet the demand. Further, these VMs and containers can be instantiated, decommissioned, or migrated providing flexibility to accommodate changes in network traffic volumes and patterns.  
* **Efficient Resource Utilization:** NFV VMs and containers optimize resource utilization by consolidating multiple functions onto shared industrial server hardware [108]. Further, these VM and container instances' resource utilization is managed by Kubernetes or other proprietary or open-source orchestrators.
* **Service Innovation:** NFV enables the rapid introduction and deployment of network services. The VNFs can be developed, tested, and deployed in the Continuous Integration/Continuous Delivery (CI/CD) approach, reducing the time-to-market of new services and features.
* **Resilience and Redundancy:** Several VNF instances are deployed on industry-grade servers in multiple locations enabled with automated failover and redundancy mechanisms for load balancing and high availability.
* **Vendor-Agnostic:** Wind farm operators can deploy the VNFs from multiple vendors in their OT networks. This reduces vendor lock-in challenges and fosters competition among the vendors, driving innovation and significantly reducing costs.
## 4 Results and Discussion
### Comparison of Scenarios 1, 2, and 3  
Three different scenarios were compared to investigate their impacts on the average evacuation time during a mine emergency. Scenario 1 considered delays due to pre-evacuation behavior and wayfinding; scenario 2 accounted for pre-evacuation delays when using a guidance device, while scenario 3 was similar to scenario 2 but included wayfinding delays.  
In the case of miners with the same stamina, the average evacuation time was highest in the chaotic scenario (scenario 1), followed by the passive signage scenario (scenario 3), with the smart evacuation scenario (scenario 2) recording the shortest average evacuation time, as depicted in Figure 14. As shown in this figure, scenario 2 achieved the shortest median evacuation time, which remained around 6 min. In contrast, scenarios 1 and 3 yielded median times of 9.6 and 9 min, respectively. On the other hand, for miners with varying stamina, significant differences were observed between the three scenarios (Figure 15). Scenario 2 showed a high divergence from scenarios 1 and 3. Scenarios 1 and 3 exhibited insignificant differences. As shown in this figure, for evacuation times across different scenarios with varying configurations, scenario 2 demonstrated the most  
Figure 11: Average evacuation time for the passive signage scenario with an increase in pre-evacuation time.  
Figure 12: Comparing case 1 and case 2 of the chaotic scenario for constant stamina.  
efficient evacuation process, with a median evacuation time of 7 min. Also, scenarios 1 and 3 reached median times of 11.1 and 10.8 min, respectively. In this analysis, the evacuation efficiency was evaluated using the smart evacuation scenario as the benchmark, comparing the median evacuation times of each scenario relative to it. The efficiency was formulated as \(\frac{Scenario\ i-Scenario\ 2}{Scenario\ 1}\times 100\), where \(i=1\) or 3. Accordingly, scenario 2 demonstrated evacuation efficiencies of 37% compared with scenario 1 and 35% compared with scenario 3.  
These results indicated that the smart evacuation scenario was the most effective method for evacuating a mine during an emergency, particularly for miners with varied stamina. In contrast, scenario 1, which included delays due to pre-evacuation behavior and wayfinding, was found to be the least effective scenario. These findings highlight the importance of considering various factors, such as pre-evacuation behavior, wayfinding, and stamina levels, that can affect the evacuation process during an emergency. Addressing these factors appropriately can lead to improved evacuation strategies and ensure the safety of all miners.  
Figure 14: Comparing scenarios 1, 2, and 3 for the same stamina.  
Figure 13: Comparing case 1 and case 2 of the chaotic scenario for variable stamina.
## 4 Experimental Environment and Results
### Experimental Results  
#### 4.2.1 Data Collection and Processing of RSSI and CSI  
The data collection and processing module include the collection and processing of RSSI and CSI. In this paper, the transmitter AP was IPtime N3004 which had three transmitting antennas. In this  
Figure 8: Schematic diagram of the indoor experiment environment at the 3 rd floor of IT-1 building.  
Figure 9: Schematic diagram of indoor experiment environment at the 2 rd floor of IT-2 building.  
paper, the transmit frequency of IPtime N3004 is 5 GHz. The receiver was a Dell Inspiron n4010 laptop. Each pair of transmitting and receiving antennas can obtain the RSSI value and CSI of 30 subcarriers. For each packet, the RSSI value on different antennas and CSI data matrix of 270-dimension can be obtained. The third antenna did not work normally and therefore we chose 180 (2 \(\times\) 3 \(\times\) 30) dimensional CSI data matrix for experiments. Figure 10 shows the changes in the RSSI values obtained on different antennas in two different indoor environments. Figure 11 shows the variation of the subcarrier values of CSI on antenna 2 in two different indoor environments.  
According to Equation (9), we process the acquired subcarriers to obtain an effective CSI value, as shown in the Figure 12.  
Figure 11: CSI subcarrier values obtained on antenna 2 in different indoor environments.  
Figure 10: RSSI values obtained on different antennas in different indoor environments.
* First, the FT-CID uses FL to accumulate the local learning models of different RPL-IIoT networks and generate a global shared parameter for edges to detect attack types of existing and unknown intrusions. * Secondly, using relational knowledge transfer learning, the FT-CID achieves a personalised IDS by efficiently transferring distributed local and global parameters between the server and IIoT network or sensors with high security. * Thirdly, the FT-CID detects and classifies intrusions under multiple classes using the customised IDS model and ensures a rich level of routing security for diverse IIoT scenarios without data leakage. * Finally, the FT-CID reflects the security of a heterogeneous IIoT environment by conducting an experimental evaluation of the IIoT dataset: advanced metering infrastructure (AMI). The remainder of this paper is organised as follows. Section 2 presents the related work, and Section 3 outlines the network architecture, system model, and attack model. Section 4 presents the proposed FT-CID design process with appropriate figures, equations, and algorithms. Section 5 shows the experimental setup and evaluation of FT-CID. Section 6 presents the experimental results. Finally, Section 7 summarises this paper.
## 3 Results
### Statistical Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multicolumn{6}{c}{**Medium-Grained Sand**} & \multicolumn{6}{c}{**Fine-Grained Silica Sand**} \\ \cline{2-11}  & **SS** & **df** & **MS** & **F** & \(p\)** & **SS** & **df** & **MS** & **F** & \(p\)** \\ \hline Type & 479.4 & 4 & 119.9 & 82.7 & \textless{}0.001 & 26.4 & 4 & 6.61 & 20.2 & \textless{}0.001 \\ Concentration & 332.3 & 6 & 55.38 & 38.2 & \textless{}0.001 & 65.3 & 6 & 10.9 & 33.2 & \textless{}0.001 \\ Interaction & 283.7 & 24 & 11.82 & 8.16 & \textless{}0.001 & 37.8 & 24 & 1.57 & 4.8 & \textless{}0.001 \\ Error & 253.5 & 70 & 1.45 & & & 57.4 & 70 & 0.33 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of two-way ANOVA (\(\alpha=0.05\)) of penetration resistance test data of Phase 1, investigating the effect of the concentration on the crust penetration resistance. Correlation between soil loss and penetration resistance.The Spearman rank correlation was established to analyse the relationship between the total soil loss (g/m\({}^{2}\)) and crust penetration resistance (N). In Phase 1, a strong negative correlation was observed for medium-grained sand (\(r(106)=-0.81\), \(p<0.001\)), and a moderate negative correlation was observed for fine-grained silica sand (\(r(106)=-0.51\), \(p<0.001\)), indicating that the soil loss tends to decrease as the penetration resistance increases. For the results of Phase 2, only weak negative correlations for medium-grained sand (\(r(80)=-0.2\), \(p=0.123\)) and fine-grained silica sand (\(r(80)=-0.1\), \(p=0.296\)) were found.
## 4 The Accurate and Stable MDTF Fault Identification Framework and Its Experimental Evaluation
### Wealthy Feature Pool Configuration Based on VWI  
In this step, a continuous wavelet transform is applied to the noise-free optimized sub-bands obtained from the LADT. The wavelet-based transforming method is used to convert time-domain optimized sub-bands to the scalograms for the enriched visualized features pool. The CWT method in this paper employs the Morlet wavelet, which is the most effective technique in the fault diagnosis approach of the rotation machines [53], as a mother wavelet function. The signal is decomposed up to 16 octaves. Based on experiments, the optimal value of voices per octave parameter was chosen as 16. The wavelet coefficients for each input sub-band, which are derived from applying the wavelet family functions in Formula (5), are used to obtain a scalogram, which is an energy distribution map of the input sub-band on a time-frequency scale. Those scalogram images of the vibration sub-bands are reshaped by the size of 224 \(\times\) 224 \(\times\) 3 for compatibility with the input layer of DCNA in the next classification step and packed to configure the enriched visual image feature pool.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Gearbox Defect Type**} & \multirow{2}{*}{**Description**} & \multicolumn{4}{c}{**Number of One-Second Samples for Specific Rotation Speed (RPM)**} & \multirow{2}{*}{**Sampling Frequency (Hz)**} \\ \cline{3-4} \cline{6-6}  & & **300** & **600** & **900** & **1200** \\ \hline Perfect Condition (PC) & Normal or perfect gearbox & 200 & 200 & 200 & 200 & 65,536 \\ \hline Defect Type 1 (DT1) & \begin{tabular}{c} 6.6\% degree of tooth defect \\ (0.6 mm/9 mm) \\ \end{tabular} & 200 & 200 & 200 & 200 & 65,536 \\ \hline Defect Type 2 (DT2) & \begin{tabular}{c} 10\% degree of tooth defect \\ (0.9 mm/9 mm) \\ \end{tabular} & 200 & 200 & 200 & 200 & 65,536 \\ \hline Defect Type 3 (DT3) & \begin{tabular}{c} 20\% degree of tooth defect \\ (1.8 mm/9 mm) \\ \end{tabular} & 200 & 200 & 200 & 200 & 65,536 \\ \hline Defect Type 4 (DT4) & \begin{tabular}{c} 30\% degree of tooth defect \\ (2.7 mm/9 mm) \\ \end{tabular} & 200 & 200 & 200 & 200 & 65,536 \\ \hline Defect Type 5 (DT5) & \
\begin{tabular}{c} 40\% degree of tooth defect \\ (3.6 mm/9 mm) \\ \end{tabular} & 200 & 200 & 200 & 200 & 65,536 \\ \hline Defect Type 6 (DT6) &
\begin{tabular}{c} 50\% degree of tooth defect \\ (4.5 mm/9 mm) \\ \end{tabular} & 200 & 200 & 200 & 200 & 65,536 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The configuration of the MDTF gearbox dataset.
## Abbreviations  
The following abbreviations are used in this manuscript:  
4G Fourth Generation Mobile Networks  
5G Fifth Generation Mobile Networks  
6G Sixth Generation Mobile Networks  
AWGN Additive White Gaussian Noise  
B5G Beyond 5G  
BER Bit Error Rate  
BPSK Binary Phase Shift Keying  
BS Base Station  
CP Cyclic Prefix  
DAB Digital Audio Broadcasting  
DCT Discrete Cosine Transform  
DFT Discrete Fourier Transform
## 5 Conclusions  
In this paper, the punching performance of 12 wallboards consisting of FAC, RAC, and various types of reinforcement were tested, and the influence of the specimen parameters on the failure mode, bearing capacity and deflection of the specimen was studied. The main conclusions are as follows:  
1. The failure characteristics of ordinary concrete wallboards and recycled concrete wallboards are similar, all of which have a clear punching shear failure, although the recycled concrete samples are more brittle than their new material counterparts, most likely due to the lower elastic modulus of the recycled concrete.
2. Under the same reinforcement ratio, the ultimate bearing capacity of common concrete wallboard is slightly higher than that of recycled concrete wallboard, with a maximum bearing capacity being 14% higher, minimum of 1%, and average of 5.8%. These relatively small differences indicate that the concrete material has little influence on the bearing capacity of a specimen.
3. When the reinforcement ratio is in the range of 0.31-0.42%, each increase of reinforcement ratio by 0.1% increases the punching ultimate bearing capacity of the wallboards by 2.48%, although the bearing capacity does not correspondingly increases after the reinforcement ratio exceeds a certain limit. In the case of equivalent reinforcement, the bearing capacity of specimens with a larger diameter and spacing are higher by 10.4% than that of the specimens with smaller diameters and spacing. Although the bearing capacities of the smaller diameter and spacing specimens are slightly lower, the deformation capacities are stronger in the later stage of failure. In actual experiments, late deformation of the specimens should be considered.
4. The finite element model of a single-row reinforced recycled concrete wallboard was established, and the damage process of the specimen analyzed. The results show that the established finite element model can accurately simulate the failure process and the ultimate failure mode of a specimen, and the calculated load-deflection curve is in good agreement with the experimental curve.  
The authors would like to acknowledge the financial support from the Key Research and Development Plan of Beijing Municipal Science and Technology Commission (Grant NO. Z161100001216012), China Postdoctoral Science Foundation (Grant NO. 71001015201705) and the Beijing Postdoctoral Science Foundation (Grant NO. Q6001015201702). [PERSON] performed the experiments and analyzed the data and wrote the paper. [PERSON] proposed the topic of this study. [PERSON], [PERSON], and [PERSON] participated in the manuscript preparation.  
The authors declare that there is no conflict of interests regarding the publication of this paper.
# The Potential of LEO Satellite-Based Opportunistic Navigation for High Dynamic Applications
###### Abstract  
Resilient navigation in Global Navigation Satellite System (GNSS)-degraded and -denied environments is becoming more and more required for many applications. It can typically be based on multi-sensor data fusion that relies on alternative technologies to GNSS. In this work, we studied the potential of a low earth orbit (LEO) satellite communication system for a high-dynamic application, when it is integrated with an inertial measurement unit (IMU) and magnetometers. We derived the influence of the main error sources that affect the LEO space vehicle (SV) Doppler-based navigation on both positioning and attitude estimations. This allowed us to determine the best, intermediate and worst cases of navigation performances. We show that while the positioning error is large due to large orbit errors or high SV clock drifts, it becomes competitive with that of an inertial navigation system (INS) based on a better quality IMU if precise satellite orbits are available. On the other hand, the attitude estimation tolerates large orbit errors and high SV clock drifts. The obtained results suggest that LEO SV signals, used as signals of opportunity for navigation, are an attractive alternative in GNSS-denied environments for high dynamic vehicles.  
navigation; signals of opportunity; Doppler shift; LEO; high dynamic; INS +
Footnote †: journal: Sensors
## 3 Result and Discussion
### Equilibrium Adsorption Isotherm  
To examine the effect of the initial NP concentration on the adsorption capacity of the modified MWCNTs, the adsorption isotherm study was carried out. Table 4 shows the adsorption isotherm models applied for the adsorption of NP onto MWCNTs. The [PERSON] [55], [PERSON] [56], [PERSON] [57], [PERSON] [54] and Temkin [58] models were respectively employed to fit the adsorption data, and the result is summarized (Table 4).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Kinetic model** & **Parameter** & **Value** & **R\({}^{2}\)** \\ \hline Pseudo-first-order & \(log(q_{e}-q_{t})=logq_{e}-\frac{k_{1}}{2.303}t\) & \(q_{e}\) (mg/g) & \(2.1\times 10^{1}\) & 0.93 \\ \cline{2-5}  & \(k_{1}\) (/min) & \(2.7\times 10^{-1}\) & \\ \hline Pseudo-second-order & \(\frac{t}{q_{t}}=\frac{1}{k_{2}q_{t}^{2}}+\frac{1}{q_{t}}t\) & \(q_{e}\) (mg/g) & \(3\times 10^{2}\) & 0.46 \\ \cline{2-5}  & \(k_{2}\) (g/mg/min) & \(1.1\times 10^{-3}\) & \\ \hline Elovich & \(q=\left(\frac{2.3}{a}\right)log(t+t_{0})-\left(\frac{2.3}{a}\right)\log t_{0}\) & a & \(4.3\times 10^{-3}\) & 0.96 \\ \cline{2-5}  & \(t_{0}=\frac{1}{ia}\) & a (g/mg/min) & \(4.3\times 10^{2}\) & \\ \hline Double exponential & \(qt=qe-\frac{D1}{ma}exp^{(-Dk_{1}t)}\) & \(Dk_{1}\) (/s) & \(1.4\times 10^{-3}\) & \\ \cline{2-5}  & \(-\frac{D2}{ma}exp^{(-Dk_{2}t)}\) & \(Dk_{2}\) (mg/L) & \(8.5\times 10^{-3}\) & \\ \cline{2-5}  & \(Dk_{2}\) (/s) & \(1.4\times 10^{-3}\) & \\ \hline \hline \end{tabular} Note: \(q_{e}\) and \(q_{t}\) are the amount of NP adsorbed at equilibrium at time \(t\) (mg/g), respectively. \(k_{1}\) and \(k_{2}\) are the rate constant of the pseudo-first-order, pseudo-second order, respectively. a is the Elovich constant and \(\alpha\) is the initial sorption rate constant for the Elovich model (g/mg/min). \(D_{1}\) and \(D_{2}\) are the 1 st and 2 nd stage concentrations (mg/L) in the DEM model, while \(Dk_{1}\), and \(Dk_{2}\) are the 1 st and 2 nd stage rate constants (1/s). Lastly, \(m_{\text{a}}\) is dosage of an adsorbent in the DEM model (g/L).  
\end{table}
Table 3: Kinetic models and parameters for adsorption of NP to MWCNTs.  
In short, [PERSON], [PERSON], [PERSON], and Elovich models fitted the data better than the Langmuir. The maximum NP adsorption capacity (\(q_{\text{max}}\), mg/g) of the modified MWCNTs estimated using the Langmuir model at pH 2 and 293 K was \(1.11\)\(\times\)\(10^{4}\) mg/g. On the other hand, a much higher \(q_{\text{max}}\) value (i.e., 25,000 mg/g) was estimated by the Elovich isotherm. Furthermore, the calculated adsorption data matched well with the observed ones. The current result demonstrated that the adsorption of NP onto the modified MWCNTs is multilayer (chemisorption) rather than Langmuir-mono-layer.
# Corpsia--A Feasible Second Best Solution?
## 3 CORSIA's Carbon Offset Market
### Price as a Signal For Climate Integrity
IRR benchmarks indicating a threshold for IRR above offset projects are considered as non-additional, because such projects would be profitable without offsets [53]. Therefore, in the following we call the difference between the internal rate of return and its benchmark the additionality indicator. Figure 6a shows the explained variation of the additionality indicator for major offset characteristics. It starts with a regression on the left hand side, where the additionality indicator is regressed on individual offset prices; from left to right, single independent variables such as the offset location are included. The figure indicates that most of the variation in the additionality indicator derives from the project type and the PDD Consultant (the firm that prepares the project design document and accompanies it through the whole process). This is in line with our findings in Section 2.3, highlighting the importance of project type with respect to climate and regulatory integrity. The project size, which is in general an important aspect for climate and regulatory integrity, is not reflected in the additionality indicator. The unconditional price, however, does not explain variation in the additionality indicator. Figure 6b plots the additionality indicator against the offset project price. The blue line shows a bivariate correlation between additionality indicator and price, indicating no correlation. The red line shows the additionality indicator and price conditional on the project type, region, size, PDD, validator, and year of establishment. The figure shows a statistically significant but economically insignificant correlation. High price increases are necessary for a slight increase in additionality. Detailed numbers for Figure 6a,b are shown in Table 1 in the Appendix A. These results indicate that offset prices cannot be used as a signal for climate integrity. Across offset projects, higher prices do not increase additionality. Within similar project types, very high price differentials are necessary to slightly increase the likelihood of additionality. Instead, a closer look at other project characteristics is necessary to reveal integrity.
###### Abstract  
Recently, the increasing demand for voice-based authentication systems has encouraged researchers to investigate methods for verifying users with short randomized pass-phrases with constrained vocabulary. The conventional i-vector framework, which has been proven to be a state-of-the-art utterance-level feature extraction technique for speaker verification, is not considered to be an optimal method for this task since it is known to suffer from severe performance degradation when dealing with short-duration speech utterances. More recent approaches that implement deep-learning techniques for embedding the speaker variability in a non-linear fashion have shown impressive performance in various speaker verification tasks. However, since most of these techniques are trained in a supervised manner, which requires speaker labels for the training data, it is difficult to use them when a scarce amount of labeled data is available for training. In this paper, we propose a novel technique for extracting an i-vector-like feature based on the variational autoencoder (VAE), which is trained in an unsupervised manner to obtain a latent variable representing the variability within a Gaussian mixture model (GMM) distribution. The proposed framework is compared with the conventional i-vector method using the TIDIGITS dataset. Experimental results showed that the proposed method could cope with the performance deterioration caused by the short duration. Furthermore, the performance of the proposed approach improved significantly when applied in conjunction with the conventional i-vector framework.  
speech embedding; deep learning; speaker recognition +
Footnote †: journal: Accepted: 12 April 2019; Published: 17 April 2019