esa-sceva/satcom-chunk-collection huggingface dataset:
# FIQ: A Fastener Inspection and Quantization Method Based on Mask FRCN
###### Abstract  
Rail-fastening components are essential for ensuring the safety of urban rail systems by securing rails to sleepers. Traditional inspection methods rely heavily on manual labor and are inefficient. This paper introduces a novel approach to address these inefficiencies and the challenges faced by computer vision-based inspections, such as missed detections due to imbalanced samples and limitations in conventional image segmentation techniques. Our approach transitions the industry's focus from qualitative to a more precise quantitative analysis of rail-fastening components. We propose Mask-FRCN, an advanced image segmentation network that incorporates three key technological enhancements: the fully refined convolutional network module (FRCN),which refines the segmentation boundaries for SFC-type fasteners; the Channel-WiseKnowledge Distillation (CWD) algorithm, which boosts the model's inference efficiency; and the FCRM methodology, which enhances the extraction capabilities for features specific to SFC-type fasteners. Furthermore, we introduce a fastener system inspection and quantization method based on the Mask FRCN method (FIQ), a novel technique for quantifying the condition of components by using image features, template matching with random forests, and a clustering calculation method derived from segmentation results. Experimental results validate that our method significantly surpasses existing techniques in accuracy, thereby offering a more efficient solution for inspecting rail-fastening components. The enhanced Mask-FRCN achieves a segmentation accuracy of 96.01% and a reduced network size of 36.1 M. Additionally, the FIQ method improves fault detection accuracy for SFC-type fasteners to 95.13%, demonstrating the efficacy and efficiency of our innovative approach.  
fastener; image segmentation; quantization; deep learning +
Footnote †: journal: Medicalinspection vehicle. The second step involves transmitting the acquired image frames to the image processing unit of the track inspection vehicle, which then determines the quality of the fasteners.
# Compact Sub 6 GHz Slot Multiantenna System for 5G Laptops
## 3 Experiment and Measurement Results
The measurement efficiency of the 5G MIMO six-antenna system is shown in Figure 8, and it ranged from 47% to 76% in the low-frequency band and 46% to 60% in the high-frequency band. Additionally, we found that Ant 1 and Ant 6 were significantly more efficient than the other antennas in the low-frequency band primarily due to their positions. Both the antennas had one side not equipped with other antennas and were therefore less affected by the other antennas. The other side that was not fitted with antennas also had more space of grounded metal, which helped to more evenly distribute the currents around the antennas. These are the reasons for the better efficiency of Ant 1 and Ant 6 compared with that of the other antennas. The measured 3D radiation patterns of the six-antenna systems operating at 3450 MHz and 4900 MHz are provided in Figure 9. The radiation patterns indicate that the far-field radiation patterns of the six antennas were similar but not identical, making the far-field radiation patterns of this design more independent. This also echoes the finding that the ECCs of the six-antenna systems were all below 0.2 in the bands of operation. This six-antenna design is therefore well suited for the operation of MIMO systems. The antenna  
Figure 6: Simulated and measured transmission coefficients for Ant 1 and Ant 2.  
patterns, which are similar but not identical, can be primarily attributed to the small differences in the positions of the antennas on the ground plane of the screen. Figure 5.0 Antenna efficiencies measured for the 5G antenna system. Figure 5.1 ECCs calculated from the complex E-field radiation patterns (**a**) simulated and (**b**) measured for the 5G antenna system. Figure 5.2 Measured 3D radiation patterns for six antennas at 3450 MHz and 4900 MHz.
# Connectivity Analysis in VANETs with Dynamic Ranges
###### Abstract  
Vehicular Ad Hoc Networks (VANETs) serve as critical platforms for inter-vehicle communication within constrained ranges, facilitating information exchange. However, the inherent challenge of dynamic network topology poses persistent disruptions, hindering safety and emergency information exchange. An alternative generalised statistical model of the channel is proposed to capture the varying transmission range of the vehicle node. The generalised model framework uses simple wireless fading channel models (Weibull, Nakagami-\(m\), Rayleigh, and lognormal) and the large vehicle obstructions to model the transmission range. This approach simplifies analysis of connection of vehicular nodes in environments were communication links are very unstable from obstructions from large vehicles and varying speeds. The connectivity probability is computed for two traffic models--free-flow and synchronized Gaussian unitary ensemble (GUE)--to simulate vehicle dynamics within a multi-lane road, enhancing the accuracy of VANET modeling. Results show that indeed the dynamic range distribution is impacted at shorter inter-vehicle distances and vehicle connectivity probability is lower with many obstructing vehicles. These findings offer valuable insights into the overall effects of parameters like path loss exponents and vehicle density on connectivity probability, thus providing knowledge on optimizing VANETs in diverse traffic scenarios.  
VANET; V2V; connectivity probability; big vehicle obstructions; synchronised traffic 2025  
2
## 5 Case-Study: Resource Fitting
### Experiment 2: FL Clients' and Server Collocated vs. Isolated
#### 5.2.2 Collocation Impact on Resource Utilization  
We also examined the resource utilization for the two setups. The results in Figure 7(a) present a similar negligible difference in compute and memory footprint. However, we observed a significant difference in network traffic for the TensorFlow framework. When the clients and server are deployed on different machines, the network throughput is higher and more stable than the collocated configuration. At the same time, we can notice a relationship between memory and network. The faster the network, the less the allocated memory. This relation can be attributed to buffering effects on the clients, who need to store packets locally before sending them to the server. Additionally, this observation suggests a correlation between TensorFlow as a network-intensive framework and the overhead of network virtualization in Kubernetes. In summary, our findings indicate that _the co-location and isolation of client and server do not significantly impact the deployment's performance or resource utilization metrics, except for the network traffic of network-intensive ML models/backends like TensorFlow_.  
Figure 8: Analysis of collocations effects for PyTorch and TensorFlow. (**a**) Resource utilization for PyTorch client, in collocated and isolated setups. No apparent variation between the two setups. (**b**) Resource utilization for TensorFlow client, in collocated and isolated setups. Insignificant variation between the two setups.
## A Comparison of Techniques for Reducing Unicast Traffic in HSR Networks  
**[PERSON], [PERSON] and [PERSON] *  
Department of Information and Communications Engineering, Myongji University, Yongin,  
Gyeonggi 449-728, Korea; E-Mails: [EMAIL_ADDRESS] (N.X.T.); [EMAIL_ADDRESS] (S.A.N.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +82-105-227-0419; Fax: +82-313-306-824.  
Academic Editor: [PERSON]  
_Received: 7 August 2015 / Accepted: 20 October 2015 / Published: 23 October 2015_  
This paper investigates several existing techniques for reducing high-availability seamless redundancy (HSR) unicast traffic in HSR networks for substation automation systems (SAS). HSR is a redundancy protocol for Ethernet networks that provides duplicate frames for separate physical paths with zero recovery time. This feature of HSR makes it very suited for real-time and mission-critical applications such as SAS systems. HSR is one of the redundancy protocols selected for SAS systems. However, the standard HSR protocol generates too much unnecessary redundant unicast traffic in connected-ring networks. This drawback degrades network performance and may cause congestion and delay. Several techniques have been proposed to reduce the redundant unicast traffic, resulting in the improvement of network performance in HSR networks. These HSR traffic reduction techniques are broadly classified into two categories based on their traffic reduction manner, including traffic filtering-based techniques and predefined path-based techniques. In this paper, we provide an overview and comparison of these HSR traffic reduction techniques found in the literature. The concepts, operational principles, network performance, advantages, and disadvantages of these techniques are investigated, summarized. We also provide a comparison of the traffic performance of these HSR traffic reduction techniques.  
high-availability seamless redundancy (HSR); HSR traffic reduction techniques; substation automation systems (SAS); smart grid communications
# Resource Allocation for GBR Services in D2D-Enabled Communication
## 3 Resource Allocation for GBR Services
### Problem Formulation  
We can now see that the channel bandwidth of channel \(l\) is different for different matchings of CU \(n\) and DU \(m\). And the optimal matching combination can be found by searching for all the possible matching combinations of CUs, DUs, and channels. Therefore, this resource allocation is a joint problem of transmit power control, sharing mode selection, channel bandwidth, and finding the optimal matching combination.  
First, we introduce \(\mathbf{P_{C}}\ =\ \left\{P_{1_{C}},\ldots,P_{N_{C}}\right\}\) and \(\mathbf{P_{D}}\ =\ \left\{P_{1_{D}},\ldots,P_{M_{D}}\right\}\) to denote the sets of the transmit power for the CUs and DUs, respectively. Second, we introduce a channel sharing indicator \(\delta_{n,m}^{l}\), which is 1 if the channel \(l\) is in a shared mode for a matching of CU \(n\) and DU \(m\), and 0 otherwise. We also introduce a matching indicator \(x_{n,m}^{l}\), which is 1 if CU \(n\) and DU \(m\) are matched on channel \(l\), and 0 otherwise. Now the original resource allocation problem can be expressed as  
\[\text{OP}:\ \min_{\mathbf{P_{C}}\mathbf{P_{D}},x_{n,m}^{l}, \delta_{n,m}^{l}} \sum_{n=1}^{N}\sum_{m=1}^{M}\sum_{l=1}^{L}x_{n,m}^{l}\!\left\{s _{n,m}^{l}B_{n,m}^{l}(s)+\left(1-\delta_{n,m}^{l}\right)\!B_{n,m}^{l}(ns)\right\} \tag{8a}\] \[s.t.\ x_{n,m}^{l}\in\{0,1\},\ \forall n,m,l\] (8b) \[\delta_{n,m}^{l}\in\{0,1\},\ \forall n,m,l\] (8c) \[\sum_{l=1}^{L}\sum_{n=1}^{N}x_{n,m}^{l}\leq 1,\ \forall n\] (8d) \[\sum_{l=1}^{L}\sum_{m=1}^{M}x_{n,m}^{l}\leq 1,\ \forall n\] (8e) \[\sum_{n=1}^{N}\sum_{m=1}^{M}x_{n,m}^{l}\leq 1,\ \forall l\] (8f) \[P_{n_{C}}\leq\mathrm{P_{max}},\ \forall n\] (8g) \[P_{m_{D}}\leq\mathrm{P_{max}},\ \forall m, \tag{8h}\]  
Constraints (8d) and (8e) say that each user is matched to no more than one user, while (8f) says that each channel takes no more than one CU-DU pair. And constraints (8g) and (8h) say that the transmit power of each user can not exceed the maximum. To solve this problem, it is necessary to repeatedly determine the channel bandwidth, transmit power, and channel sharing mode for all the possible matching combinations. This requires very high computational complexity because the number of matching combinations increases exponentially as the number of users increases.  
In this paper, once we determine the channel bandwidth of all the possible matchings together with transmit power and channel sharing mode in advance, the remaining problem is reduced to the 3-D matching problem of CU, DU and channel using the calculated channel bandwidths of all possible matchings. The key notations and symbols are listed in Table 1 in Appendix A.
# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 4 Research Methodology
### Phase 2. Conducting the Review
\begin{table}
\begin{tabular}{p{34.1 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline
**Study Number** & **Title** & **Authors** & **Year** & **Publisher** & **Type** \\ \hline S7 & Physical layer authentication of Internet of Things wireless devices using convolutional neural networks and recurrence plots & [PERSON] et al. (2018) & Wiley & Journal \\ S8 & An Identity Authentication Method of a MIoT Device Based on Radio Frequency (RF) Fingerprint Technology & [PERSON] et al. (2020) & MDPI & Journal \\ S9 & Injecting Reliable Radio Frequency Fingerprints Using Metasurface for the Internet of Things & [PERSON] et al. (2021) & 2021 & IEEE & Journal \\ S10 & Securing Wireless Communication Using RF Fingerprinting & [PERSON] et al. (2019) & 2021 & IEEE & Conference \\ S11 & Radio Identity Verification-Based IoT Security Using RF-DNA Fingerprints and SVM & [PERSON] et al. (2020) & 2020 & IEEE & Journal \\ S12 & A LSTM-Based Channel Fingerprinting Method for Intrusion Detection & S13 & Identification of Wireless Transceiver Devices Using Radio Frequency (RF) Fingerprinting Based on STFT Analysis to Enhance Authentication Security & [PERSON] et al. (2021) & IEEE & Conference \\ S14 & Wi-SUN Device Authentication using Physical Layer Fingerprint & Deep-Learning-Aided RF Fingerprinting for NFC Security & [PERSON] et al. (2020) & 2021 & IEEE & Journal \\ S16 & A Learning Approach for Physical Layer Authentication Using Adaptive Neural Network & S17 & A Novel Physical Layer Authentication Method with Convolutional Neural Network & S18 & Automated Labeling and Learning for Physical Layer Authentication Against Clone Node and Sybil Attacks in Industrial Wireless Edge Networks & S19 & Threshold-Free Physical Layer Authentication Based on Machine Learning for Industrial Wireless CPS & S20 & Physical layer Channel Authentication for 5G via Machine Learning Algorithm & S21 & PHY-Layer Spoofing Detection With Reinforcement Learning in Wireless Networks & S22 & Multiuser Physical Layer Authentication in Internet of Things with Data Augmentation & S23 & Machine Learning-based Physical Layer Authentication using Neighborhood Component Analysis in MIMO Wireless Communications & S24 & Physical Layer Authentication with Cooperative Wireless Communications and Machine Learning & S25 & Physical Layer Authentication Algorithm Based on SVM & S26 & Physical Layer Authentication Mechanism for IoT Devices & S27 & Multiuser physical layer security mechanism in the wireless communication system of the IIOT & S28 & Physical layer Authentication Using Channel State Information and Machine Learning & S29 & Deep-Learning-Based Physical Layer Authentication for Industrial Wireless Sensor Networks & S30 & Physical Layer Security: Detection of Active Eavesdropping Attacks by Support Vector Machines & S31 & RF-PUF: Enhancing IoT Security through Authentication of Wireless Nodes using In-situ Machine Learning & & & \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._
## 3 Inverter Modeling and Control
### Analytical Model of Induction Motor  
The Ims are commonly accepted as a more suitable option for electric vehicle application because of their rigidity, reliability, lower cost, and less maintenance. Nevertheless, Ims' speed as well as torque controls are quite complicated because of their non-linear and complex structure. Based on IM's dynamic model, various control strategies have been developed to surpass control challenges. Hence, the dynamic model of an IM is important for the design of a control approach to achieve high efficiency. Therefore, the dq model of IM is considered in the simulation design to implement closed-loop IFOC for the motor drive. The analytical model of IM has been described in synchronous reference frame [5,6] by means of the Equations (23)-(29) [27] as follows.  
\[v_{qs}^{e}=R_{S}i_{qs}^{e}+L_{ls}\frac{di_{qs}^{e}}{dt}+L_{m}\frac{di_{qm}^{e} }{dt}+\omega_{e}(L_{ls}i_{ds}^{e}+L_{m}i_{dm}^{e}) \tag{23}\]  
\[v_{ds}^{e}=R_{S}i_{ds}^{e}+L_{ls}\frac{di_{ds}^{e}}{dt}+L_{m}\frac{di_{dm}^{e} }{dt}-\omega_{e}(L_{ls}i_{ds}^{e}+L_{m}i_{dm}^{e}) \tag{24}\]  
\[v_{qr}^{e}=0=R_{r}i_{qr}^{e}+L_{lr}\frac{di_{qr}^{e}}{dt}+L_{m}\frac{di_{qm}^{ e}}{dt}+\omega_{sl}(L_{lr}i_{dr}^{e}+L_{m}i_{dm}^{e}) \tag{25}\]  
\[v_{dr}^{e}=0=R_{r}i_{dr}^{e}+L_{lr}\frac{di_{dr}^{e}}{dt}+L_{m}\frac{di_{dm}^{ e}}{dt}-\omega_{sl}\left(L_{lr}i_{dr}^{e}+L_{m}i_{qm}^{e}\right) \tag{26}\]  
\[0=-R_{fe}i_{qfe}^{e}+L_{m}\frac{di_{qm}^{e}}{dt}+\omega_{e}L_{m}i_{dm}^{e} \tag{27}\]  
\[0=-R_{fe}i_{qfe}^{e}+L_{m}\frac{di_{dm}^{e}}{dt}-\omega_{e}L_{m}i_{qm}^{e} \tag{28}\]  
\[T_{e}=\frac{3}{2}\frac{P}{2}\frac{L_{m}}{L_{r}}\left(i_{qs}^{e}i_{dr}^{e}-i_{ ds}^{e}i_{qr}^{e}\right)=T_{l}+B\omega_{r}+J\frac{d\omega_{r}}{dt} \tag{29}\]  
where \(R_{fe}\) (\(\Omega\)) is the basic resistance and the dq variables (\(v_{qs}^{e},\ v_{ds}^{e}\)) (V) and (\(i_{qs}^{e},\ i_{ds}^{e}\)) (A) are stator voltages and the stator currents, respectively. Moreover, dq variables (\(v_{qr}^{e},\ v_{dr}^{e}\)) (V) and (\(i_{qr}^{e},\ i_{dr}^{e}\)) (A) are rotor voltages and the rotor currents, respectively. \(\omega_{r}\) (rad/s) is the angular speed of IM, \(\omega_{e}\) is synchronous speed, \(\omega_{sl}\) is slip speed, and \(P\) represents the pole numbers. \(R_{s}\) (\(\Omega\)), \(R_{r}\) (\(\Omega\)), \(L_{ls}\) (H), and \(L_{m}\) (H) are stator and the rotor resistances, stator leakage, rotor leakage, and mutual inductances, respectively. \(L_{r}\) is the rotor self-inductance (\(L_{r}=L_{lr}+L_{m}\)). Lastly, \(T_{e}\) (Nm), \(T_{l}\) (Nm), \(B\) (Nms), and \(J\) (\(\frac{\mathrm{kg}}{\mathrm{m}^{2}}\)) are electrical and load torques, coefficient of friction, and the moment of inertia, respectively.
# Advanced Persistent Threats and Their Defense Methods in Industrial Internet of Things: A Survey
## 3 APTs Faced by IIoT and Its Defense Methods
### Platform Layer  
The cloud platform layer is the core layer of the IIoT architecture, responsible for data storage, analysis, processing, and management. In the cloud platform layer, APT attacks may include intrusion into cloud servers, theft of cloud storage, and tampering with data analysis. The following are several aspects of these attacks.  
* Physical layer attacks [48; 49]: Attackers may use physical layer attack methods such as equipment intrusion and the interception of data streams to undermine the integrity and reliability of the IIoT platform. This type of attack can continue undetected and have a significant impact on the IIoT platform;
* Data leakage [59]: Due to the large amount of sensitive data that the IIoT platform needs to process, data leakage is a serious threat. Attackers may exploit weaknesses to steal sensitive information and use it for other attacks, such as identity theft and ransomware;
* Supply chain attacks [18; 60]: Supply chain attacks involve attackers infiltrating the IIoT platform through third-party service providers or components in the platform's supply chain. This type of attack can last for a long time and cause significant damage to the platform without being detected.  
To address these threats, companies should implement security measures on the IIoT platform, such as access control, authentication, encryption, vulnerability management, and security auditing. In addition, companies should maintain security patch updates, strengthen the monitoring of the IIoT platform, and provide effective cybersecurity training for employees.
# Evaluation of Noise-Reduction Techniques for Gas-Turbine Test Stands: A Preliminary Analysis
## 7 Conclusions
The comparison graph of average insertion loss indicates that a three-baffle staggered arrangement is the most suitable choice for noise attenuation in gas-turbine test-stand applications, although an increase in self-generated noise was observed in flow conditions. Additionally, comparisons of sound-pressure levels revealed that the staggered configuration provided more attenuated acoustic fields than the linear configurations. Pressure-loss assessments demonstrated that linear configurations generally exhibited lower pressure losses as compared to the staggered configurations, which was also obvious. Staggered arrangements induced higher pressure losses due to increased flow complexity and the interactions between jets and deflector surfaces. The acoustic field analyses highlighted the impact of sharp-edged deflectors on flow separation, turbulence generation, and subsequent noise generation. The staggered configurations exacerbated these effects, leading to significantly higher noise levels than the linear arrangements. Overall, this study provided new insights into the acoustic and aerodynamic performance of lamellar attenuators for gas-turbine test-stand applications. Further research should focus on acoustic simulations under more flow conditions and optimizing attenuator geometries for further enhancement of the performance with respect to flow characteristics. Conceptualization, L.C. and M.D. ; methodology, M.D. ; software, M.D. ; validation, L.C. and M.D. ; formal analysis, L.C. ; investigation, L.C. ; data curation, M.D. ; writing--original draft preparation, L.C. and M.D. ; writing--review and editing, L.C. and M.D. ; visualization, M.D. ; supervision, L.C. All authors have read and agreed to the published version of the manuscript. This work was carried out through the Nuclear Program within the National Plan for Research, Development, and Innovation 2022-2027, conducted with the support of MCID, project no. PN23.12.01.01. Informed Consent Statement: Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to internal institutional restrictions. The data presented and analyzed in this report were obtained with the help of INCDT COMOTI's Research and Experiments Center in the field of Acoustic and Vibrations staff and facility, and the work was carried out through the anechoic chamber and measurement equipment. The authors declare no conflicts of interest. The funders had no role in the design of the study, in the collection, analyses, data interpretation, the writing of the manuscript, or in the decision to publish the results.