esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
## 3 The Composition of the INS
### INS Components  
Table 2 presents the INS components together with their alternative names, where applicable, and the relevant IMO document. Alternative names are used in manufacturer brochures or different IMO resources. For instance, the \"Speed and Distance Measuring Device (SDMD)\" is mentioned in the SOLAS regulation V/19. This device is also called \"speed log\" in the maritime industry [45]. Different names, other than those used in this study may also exist. References (i.e., the columns titled \"IMO Document\" and \"Paragraph or Appendix\") may not be limited to only those given in the table.  
Many IMO documents list the Electronic Position Fixing System (EPFS) [5; 46; 47]. The EPFS provides position information of the vessel continuously. Two groups are distinguished, namely terrestrial (e.g., enhanced Long-range Navigation (eLORAN)) and aerial (e.g., Global Navigation Satellite System (GNSS)). Several variations of the GNSS exist, such as the U.S. based-GPS, the Russia- based Global'naya Navigationnaya Sputnikovaya Sistema (GLONASS), China's Beidou Weixing Daohang Xitong (BeiDou), and the European Union's Galileo [48]. Only the GPS is considered in this study.  
The IMO describes the performance standards of the selected components comprehensively. The functions of the selected components may be enhanced due to the latest performance standards of the INS described in Resolution MSC.252(83). The features of the selected components are briefly described below; a complete listing is available in the Appendix A.
# Sound Localization Framework for Construction Site Monitoring
## 6 System Validation on a Real Site
### Exterior Environment Tests  
Exterior environment tests were performed at a reinforced concrete apartment construction site. With the foundation placement completed, a tower crane installation point was set as the center, and microphones were installed.  
Figure 11: Geometry of the interior construction site validation test 2.  
Figure 10: Geometry of construction site validation test 1.  
In the exterior tests, the maximum test radius was set at 10 m--the same as that used in the interior environment tests (Figure 12). The devices were connected to each other through LAN cables, and the maximum test radius was set as the maximum length of the LAN cable (i.e., 10 m). If the length of the LAN cable in the exterior environment tests is changed, the data transmission speed will vary, thereby affecting the localization values. Therefore, the length was limited to 10 m.
## 4 Experiments and Result Analysis
### Comparative Analysis  
After training, the roc_auc scores of SVM, NB, and DT on the test set are 0.5, 0.5604, and 0.9249, respectively. On the test set, the proposed method's roc_auc score is 0.9935, which are 0.4935, 0.4331, 0.0686 higher than the scores of the other three classifiers. Because [PERSON] work lacks roc_auc scores, it is not compared here. The results of the comparison show that the proposed RF classifier outperforms other classifiers in terms of classification performance.  
We also compare the evaluation metrics of SVM, NB, DT and our proposed classifier. The comparison results are shown in Table 7. The RF classification model proposed in this paper outperforms SVM, NB, DT in terms of accuracy, precision, recall, and F1-score. The RF classifier outperforms the other three in terms of precision by 38%, 29%, and 3%, respectively. The RF classifier has a recall rate that is 21% higher, 36% higher, and 4% higher than the other three. The F1-score for the other three classes of classifiers are 31%, 34%, and 3% lower than the RF classifier, respectively.  
The JRipper + Adaboost classification method with the best classification effect in literature [18] is chosen for comparison because the performance of the RF classification method in [PERSON] work is not ideal. Table 7 demonstrates that the intrusion detection method of power industrial control systems proposed in this paper has 2% higher accuracy, 3% higher precision, 4% higher recall rate, and 3% higher F1-score when compared to the method in literature [18]. Compared to the method in the literature [28], as shown in Table 7, the accuracy is 2% higher, the precision is 2% higher, the recall rate is 3% higher, and the F1-score is 3% higher, the proposed method outperforms the method in [28] comprehensively.
# A Novel IEEE 802.15.4e DSME MAC for Wireless Sensor Networks
## 5 Performance Analysis  
The probability of the bit error rate \(P_{b}(\zeta)\) for the transceiver of IEEE 802.15.4 radios is calculated [1] as given in the following equation.  
\[P_{b}(\zeta)=\frac{8}{15}\frac{1}{16}\sum_{k=2}^{16}(-1)^{k}\binom{16}{k}e^{2 0\zeta(\frac{1}{k}-1)} \tag{15}\]  
where, \(\zeta\) is the signal-to-interference-plus-noise ratio (SINR). Let, \(L_{D}\) be the length of the data packet and \(L_{Ack}\) be the length of the acknowledgement packet. Let, \(q\) be the probability that a device has a packet to transmit and \(\alpha_{1}\) be the probability that the first CCA is busy due to data transmission by other devices. A device starts transmission, if the channel is found ideal. Hence,  
\[\alpha_{1}=L_{D}(1-(1-\phi)^{M-1})(1-\alpha)(1-\beta) \tag{16}\]Coordinator will send the acknowledgement packet, if it receives the packet without any collision or channel error. In other words, we can say if exactly one device transmits without any channel error, then the coordinator will send the acknowledgement packet. Let \(\alpha_{2}\) be the probability that the first CCA is busy due to transmission of acknowledgement by the coordinator. Hence,  
\[\alpha_{2}=M\phi(1-\phi)^{M-1}(1-\alpha)(1-\beta)(1-P_{b}(\zeta))^{L_{Data}} \tag{17}\]  
where, \(L_{Data}\) is the length of the data packet in bits. Let, \(\alpha\) be the probability that the first CCA is busy. During the first CCA, the device will find the channel busy due to the transmission of data or acknowledgement. Hence, the probability of finding the channel busy during the first CCA is given as follows.  
\[\alpha=[L_{D}(1-(1-\phi)^{M-1})+M\phi(1-\phi)^{M-1}(1-P_{b}(\zeta))^{L_{Data}} ](1-\alpha)(1-\beta). \tag{18}\]  
Let \(\beta\) be the probability that the second CCA is busy. A device will perform the second CCA, if the channel is found ideal during the first CCA. A device transmits the data, if the channel is found idle after the second CCA. In our scheme, the channel will be busy during the second CCA due to data transmission as the acknowledgement packet is avoided by keeping two ideal slots between two CCAs. Hence, the tagged device will find the channel busy during its second CCA, if and only if any other device completes his second CCA and starts transmitting data. Hence,  
\[\beta=3(1-(1-\phi)^{M-1})(1-\beta). \tag{19}\]  
The device will go for the next backoff, if the channel access fails. Hence, the probability of going to the next backoff is  
\[P_{backoff}=p+(1-p)\alpha+(1-p)(1-\alpha)\beta. \tag{20}\]
## 2 Experimental Analysis
### Results and Discussions
From these studies, it is generally clear that the accurate positioning of an AE event is realized by adjusting the number of sensors and improving the picking accuracy of the first arrival of the signal. As a matter of fact, the highest accuracy can be achieved for damage sources localized among the sensor distributions. Distances of sources far from the sensor network are less accurate. Onset time determination of an AE signal is another important factor in this analysis. A widely used approach, based on the Akaike information criterion (AIC), is confirmed to provide more reliable onset time determination of AE signals, see [53]. Moreover, the  
Figure 9: (**a**) AE sources distribution along the \(z\) direction: Lunitek system (see Figure 4); (**b**) Mistras system between M1 and M2 sensors. inhomogeneity of the test object may lead to errors in the ultrasonic wave travelling model. Different-scale fluctuations and structure variations in the composite structure can result in random variations of the propagation velocity and systematic errors. An example of the difference in the accuracy of the experimental results on AEs is provided in Figure 9 which depicts the distribution of AE sources' localization along the \(z\) direction of the cube sample. Most importantly, the AE activity is preponderant along the positive semi-axis in both plots obtained by Lunitek and Mistras systems. Therefore, the main goal of the present article, rather than focusing on the Lunitek and Mistras systems' accuracy, is to compare the two systems' outcomes. This comparison is important because the Lunitek and Mistras systems present different configurations and, consequently, different outputs, but in both cases, the global parameter evolution indicates the same trend, which is the greatest advantage of the AE technique. Figure 10 presents a comparison between the signals captured over the two opposite faces of the cube where the sensors for both acquisition systems were placed together: at \(z=-15\) cm (Lunitek L1 and L2, Mistras M1), and \(z=15\) cm (Lunitek L5 and L6, Mistras M2), as depicted in Figure 4b. A significant difference in signals counts for the Mistras sensors indicates that the damage developed more intensely close to the sensor M2. Such a difference is not as clear for the Lunitek system. This discrepancy is likely due to the location of the sensors on the cube's faces: the Mistras sensors are near the center of each face, whereas the Lunitek sensors are close to the specimen vertices. Aside from using the experimental data sets from both acquisition systems, the damage analysis methods were also applied to the simulation results, as described in Section 3.
# Security Vulnerabilities in LPWANs--An Attack Vector Analysis for the IoT Ecosystem
## 3 State-of-the-Art Review  
IoT technologies have grown over the past years increasing the quality of human life in different application domains, namely, everyday applications (smart home, smart transportation, smart education, smart cities), economy applications (mining fields, oil and gas fields, productivity in factories), health care and security applications [36; 37; 38; 39].  
The existing IoT connectivity standards, for example, in IEEE 802.15.1 and IEEE 802.15.4 have been generally used, but their short communication range has been announced as the main drawback [40]. Alternatively, cellular networks that allow a wide connectivity range present cost and complexity as their main drawback. Thus, LPWAN has granted a viable option to the diversified shortcomings of these standards.  
With LPWAN use cases growing [41], it is crucial to assess the security mechanisms of these technologies. Multiple LPWAN technologies are available using different frequencies and transmission mechanisms, however, all of these types of communications have their own set of resources and security mechanisms for authenticity, confidentiality, and data integrity [42]. Even with the valuable characteristics that LPWAN technologies present, security and privacy issues still the biggest challenge for their large-scale implementation [40]. Due to their heterogeneity, ubiquity, and easy accessibility to devices in the network, LPWANs vulnerabilities continue to increase, leading to new threats and types of intrusions [39]. Security is a major concern in the IoT ecosystem, where LPWAN communication technologies play a crucial role. These types of technologies increase the attackers' range of action due to their long-range connection and high transmission time. Each device connected to the network is a possible vulnerable point where each type of technology uses its security mechanisms to establish secure communications [43].  
In this section, the main vulnerabilities, threats, attacks, and defense strategies to these gaps in LPWAN networks will be presented, through a state-of-the-art review.
# Towards Understanding Driver Acceptance of C-ITS Services--A Multi-Use Case Field Study Approach
## 1 Introduction  
Over the past 15 years, C-ITS (Cooperative Intelligent Transport Systems) services have undergone extensive specification, deployment, and testing, leading to their first commercial applications and rollouts in Europe. C-ITS implies the cooperation of all connected traffic participants through data exchange. The most common cooperation types are Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I), and vice versa, I2V.
# Improvement of Porous GaN-Based UV Photodetector with Graphene Cladding
## 2 Materials and Methods
### Synthesis of Graphene  
Both CVD and the electrochemical method were used to synthesize graphene in this work. In the CVD growth, Cu foil (6.35 \(\times\) 6.35 mm\({}^{2}\), Strem, >99.9%) was loaded into a horizontal tube furnace. The furnace temperature subsequently was raised to 800 \({}^{\circ}\)C. The Cu foil was subjected to H\({}_{2}\) annealing (flow rate of 150 standard cubic centimetres per minute (sccm)) for 10 min to improve its crystallinity. Next, heptane vapour and H\({}_{2}\) gas were channelled into the furnace with (1 standard cubic foot per hour (scfh) and 150 sccm, respectively) to initiate graphene growth. The process lasted for 30 min. Finally, the furnace was cooled down naturally, and the graphene/Cu sample was removed.  
Electrochemically exfoliated graphene was performed in a two-electrode electrochemical cell, with a graphite rod being the anode, while that of Pt was the cathode. A mixture of H\({}_{2}\)SO\({}_{4}\) and (NH\({}_{4}\))\({}_{2}\)S\({}_{2}\)O\({}_{8}\) (both 0.25 M) would serve as the electrolyte. A total of 2.5 V was applied across the electrodes to trigger the wetting of the graphite rod. After 5 min, the potential was raised to 5 V, which initiates the exfoliation process. Exfoliated graphene sediments were notably seen within 30 min. Next, the sediments were vacuum filtered and washed several times to remove traces of electrolyte. Finally, the graphene powder was dried and stored in a vial.
# On the Competitiveness of Oblivious Routing:
## 4 New Performance Metrics for Static Routing
### Traffic Model  
In order to evaluate the PoC and the EVoC, we need to be able to characterize the probability distribution \(\Pr(\theta)\) by which a traffic matrix \(\theta\) shows up in the network. To reflect that, the oblivious routing algorithm does not assume any a priori information on input traffic, and our traffic model assumes that traffic matrices arrive with equal probability from some pre-defined input set.  
**Assumption 1**.: _The probability \(\Pr(\theta)\) that a traffic matrix \(\theta\) is realized by the users is given by \(\Pr(\theta)=\frac{1}{\operatorname{Vol}(\Theta)}\), where \(\Theta\) is some pre-defined set of traffic matrices, and \(\operatorname{Vol}\) denotes the standard Lebesgue-measure (i.e., the K-dimensional volume)._  
Here, \(\Theta\) might be confined by the topology of the network (e.g., the capacity of the links at the border), it might be shaped by some admission control mechanism at the network ingress, or it might be any bounded subset of \(\mathbb{R}^{K}_{+}\). For the rest of this paper, we assume that \(\mathbb{R}^{K}_{+}\supset\Theta\) is compact and polyhedral. The very definition of the performance measures, and the essential methodology we introduce later on to calculate them, remain valid even if Assumption 1 fails, but the development becomes somewhat more complex.
# Seismic Design and Ductility Evaluation of Thin-Walled Stiffened Steel Square Box Columns
## 6 Proposed Design Equations  
The ultimate strengths and ductility of 40 analyzed columns were determined through finite element (FE) analysis using the 2 SM for material nonlinearity in ABAQUS. Figure 12 plots \(H_{m}/H_{y}\) against the integrated parameters \((1+P/P_{y})\cdot R_{f}\lambda\), considering the interaction between \(R_{f}\), \(\lambda\), and \(P/P_{y}\) on column strength. The best-fitting equation relating to the computed ultimate strength is given by:  
\[\frac{H_{m}}{H_{y}}=\frac{0.1402}{\left[\left(1+\frac{P}{P_{y}}\right)R_{f} \lambda\right]^{1.084}} \tag{9}\]  
As shown in Figure 12, the ultimate strength improved as the integrated parameters \((1+P/P_{y})\cdot R_{f}\lambda\), decreased. For this study, failure of the thin-walled stiffened steel box columns was considered to occur at a displacement equal to either \(\delta_{m}\) or \(\delta_{0.95}\). Here, \(\delta_{m}\) represents the maximum displacement corresponding to \(H_{m}/H_{y}\), while \(\delta_{0.95}\) is the displacement at which post-peak strength drops to 95% of \(H_{m}/H_{y}\). These normalized parameters are key to evaluating the ductile behavior of the columns. The \(\delta_{0.95}/\delta_{y}\) parameter was used to analyze ductility, as column strength significantly deteriorates after this point due to local buckling.  
Figure 11: Post buckling strength and ductility of the columns.  
Figure 12: The ultimate strength of the analyzed columns.  
Figure 13a,b illustrate a reduction in both maximum deformation and post-buckling deformation as \((1+P/P_{y})\cdot R_{f}\lambda\) and \(R_{f}\lambda\) increased. Additionally, ductility equations were proposed:  
\[\frac{\delta_{m}}{\delta_{y}}=\frac{0.5654}{\left(R_{f}\lambda\right)^{0.938}} \tag{10}\]  
\[\frac{\delta_{0.95}}{\delta_{y}}=\frac{1.0877}{\left[\left(1+\frac{P}{P_{y}} \right)R_{f}\lambda\right]^{0.783}} \tag{11}\]  
Equations (10) and (11) were derived from the design parameters to determine maximum deformation and post-buckling deformation, respectively, within the ranges \(0.2\leq R_{f}\leq 0.50\), \(0.2\leq\lambda\leq 0.50\), and \(P/P_{y}\leq 0.2\). These limits for parameters are in the practical range for bridge design [44,46].
## 3 Specific Devices
### Vertebral Body Stenting System(r)  
The Vertebral Body Stenting System(r) (VBS) is a titanium device designed for percutaneous vertebral augmentation. It aims to relieve pain and restore the height of fractured vertebrae, helping to correct spinal curvature [12]. The system uses a balloon, similar to standard kyphoplasty, which can expand up to 400% within the vertebral body. By employing ligamentotaxis, the fracture is reduced, restoring vertebral height and creating space for the injection of highly viscous PMMA. After expansion, the balloon is deflated and removed, while the stent remains in place to prevent height loss [13].  
VBS is indicated for treating osteoporortic vertebral compression fractures from T10 to L5, particularly in cases without posterior vertebral edge involvement. It is suitable for Genant grade 2 and 3 fractures with kyphotic angulation greater than 15\({}^{\circ}\), as well as fractures classified as A1.1, A1.2, A1.3, and A3.1 under the AO classification [11; 14]. Contraindications include fracture types A2, A3.3, B1.1, B2.1, B3, and C.  
Extensive mechanical testing has demonstrated VBS's ability to minimize height loss after balloon deflation, performing better than balloon kyphoplasty [15]. VBS maintains pre-fracture vertebral height and shows improved clinical outcomes, with significant reductions in pain (VAS down by 6.4 points) and disability (ODI decreased by 41.7%) at 12 months. Additionally, vertebral body height improved by 15.3%, and kyphotic correction reached 4.5\({}^{\circ}\). The risk of adjacent vertebral fractures was around 9%, similar to or slightly lower than balloon kyphoplasty rates. Overall, VBS is a safe and effective technique for treating vertebral fractures, with a low incidence of adverse events [12; 15].
# Analysis of Turbulence Parameters of Typhoon Morakot along the Southeast Coast of China
## 1 Introduction
### Computational Fluid Dynamics  
Wind tunnel tests, computational fluid dynamics (CFD), and field measurements can all be used to explore wind effects on structures. CFD is now used as a supplement to traditional wind engineering methodologies (wind tunnel tests and field measurements), such as the large-eddy simulation (LES) approach [5, 6, 7]. [PERSON] et al. [8] employed LES and wind tunnel tests to conduct verification research on the wind effect of urban super high-rise buildings. The variable wind loading predicted by LES on a building model agreed well with wind tunnel tests utilizing a new inflow turbulence generation method [9]. [PERSON] et al. [10] used CFD to predict wind loads and wind flows around a building. [PERSON] et al. [11] investigated turbulent boundary layers over a smooth and rough two-dimensional hill using LES, followed by a discussion on the applicability of turbulencemodels. [PERSON] et al. [12] used LES to analyze the turbulent boundary layer over two-dimensional hills with two different mountain types and roughness conditions, and they verified the numerical analysis by comparing experimental wind tunnel data. [PERSON] et al. [13] used numerical simulation to propose a new kind of turbulent kinetic energy profile as the inlet boundary condition based on the SST K-\(\upomega\) turbulence model and analyzed the wind speed characteristics at different terrain heights. [PERSON] investigated a method for improving the drag coefficient performance of an aerodynamic modification [14, 15]. Many researchers have used the CFD method to investigate the effect of terrain on wind properties at a bridge and obtained some significant results [16, 17, 18]. The CFD method was used to simulate the wind field characteristics at the deep gorge bridge site with high altitude and considerable temperature changes [16]. [PERSON] et al. [19] investigated the wind interference characteristics of six two-row buildings at various distances (including S\(x\) and S\(y\)) by combining wind tunnel tests and numerical simulations. Using the CFD method, [PERSON] and [PERSON] et al. [20, 21] examined the effect of complicated terrain on atmospheric turbulence flow. Although numerical simulations can yield good results, field measurements are still essential for understanding wind characteristics.
## 3 Datasets and Methodology
### Methodology
#### 3.2.5 Optical Fiber Monitoring Scheme  
A borehole with a depth of 500 m and a diameter of 150 mm was constructed (Figure 3). Along the entire depth of the borehole, anchor-type fixed-point densely distributed strain-sensing optical fibers were deployed at 5 m intervals. These fibers were connected at the bottom with high-strength optical leads to form a U-shaped loop. Additionally, within 20 m of the borehole collar, low-mode strain-sensing optical fibers were installed and similarly configured into a U-shaped loop with high-strength leads. Furthermore, non-metallic reinforced dense distributed temperature-sensing optical fibers were installed within the upper 20 m of the borehole for single-ended temperature measurements. The sensing cables were guided into the borehole using a weighted guiding head, and the borehole was subsequently backfilled with specialized grouting material. All optical lines were integrated into the surface monitoring station to establish an automated monitoring system.  
Figure 3: Schematic diagram of the optical fiber sensing deployment in a 500 m deep borehole for automated monitoring.