esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Included Studies Based on GT and BN for Safety and Security
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Application Sector** & **Technique** & **Description** \\ \hline [PERSON] et al. [63] & Systems of Systems (SoS) & BN & Development of an approach to determine cyber-attacks propagation in SoS. \\ \hline [PERSON] et al. [64] & Vehicle Transportation & BN & To develop a framework for trust model using ML and DL for vehicle transportation. \\ \hline [PERSON] et al. [65] & Oil and Gas Sector & BN & This research identifies risk in the oil and gas sector by proposing a graphic model and BN approach. \\ \hline [PERSON] et al. [66] & Railways & BN & A risk identification method based on BN for metro construction is developed. \\ \hline [PERSON] et al. [67] & Vehicle Transportation & GT & A graph enabled based risks recognition approach in vehicle-vehicle communication. \\ \hline [PERSON] et al. [68] & Gas Pipelines & GT & Development of a method to examine the reliability in gas pipeline systems. \\ \hline [PERSON] et al. [69] & Chemical Industry & BN & A probabilistic risk assessment method is developed based on BN to monitor threats in the chemical industry. \\ \hline [PERSON] et al. [70] & Oil wharf Handling & BN & To develop a risk analysis model based on a static incident approach. \\ \hline [PERSON] et al. [71] & Nuclear & BN & A methodology is proposed for diagnostic outcomes from BN model for risk assessment. \\ \hline [PERSON] et al. [72] & Gas and Pipelines & BN & A novel methodology is proposed for vulnerability calculation of gas pipelines. \\ \hline [PERSON] et al. [73] & Networks Security & BN & Development of vulnerability analysis method that may eliminate the cyber-attacks. \\ \hline [PERSON] et al. [74] & Socio-Technical Systems & BN & This study aims to examine what factors could influence sociotechnical systems. \\ \hline [PERSON] et al. [75] & Chemical Industry & BN & The developed approach addresses the vulnerability evaluation using BN model. \\ \hline [PERSON] et al. [76] & Navigation Environment & BN & Establishment of an index system by integrating BN with fuzzy theory to offer safety evaluation. \\ \hline [PERSON] et al. [77] & Chemical Industry & BN & Development of vulnerability analysis approach methodology for monitoring intentional attacks. \\ \hline [PERSON] et al. [78] & Unmanned Aerial Vehicles (UAV) & BN & A technique to solve issues related to software risks and failures are developed by using BayesianLab. \\ \hline [PERSON] [79] & Complex Systems & GT & A polynomial-time system is proposed to recognize critical nodes for ensuring security in complex systems, such as the power and energy sectors. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline
# Method for Estimating the Optimal Coefficient of L1C/B1C Signal Correlator Joint Receiving
## 4 Experiment and Analysis  
We used static test equipment with a sampling rate of 250 MHz and an intermediate frequency of 62.5 MHz. A small atomic clock was adopted by the GNSS signal collector, as shown in Figure 9, which was placed in the open area on the roof to collect signals in Luo nan, Shaanxi. This is a dual-channel acquisition card developed by VI SERVICE NETWORK. Figure 10 shows the sky plot distribution of observable satellites during the test. In the positioning module, static single-point positioning was used to verify the improved result of combined positioning. The real-life satellite data that were collected were 60s long, and the first 55 s were used in this experiment.  
Figure 10: The Sky plot of data collected in Luo-nan at 23:31:26 on 25 January 2021.  
Figure 9: Multi-channel RF signal synchronization collector (VISN-7678).  
The real-life satellite data were used in subsequent experiments to verify the five receiving schemes listed in Table 3 in terms of tracking accuracy, carrier-to-noise ratio, and positioning accuracy.
## 4 Stability Evaluation
### Result Analysis
#### 4.3.2 Distribution of Plastic Zone  
The plastic zone serves as a crucial indicator for assessing the damage and failure of the salt cavern's surrounding rock. Figure 12 presents the distribution patterns of the plastic zones in the salt cavern's surrounding rock under various internal pressures. Evidently, when the internal pressure is low, the plastic zone at the cavern wall exhibits a widespread distribution. In the case of the large-height-difference BWSC, the plastic zone at the top of the deep cavern is more extensive than that of the shallow cavern. This suggests that the top of the deep cavern is more vulnerable to damage. Such findings hold significant implications for determining the setting depth of the casing shoe during the subsequent well-drilling process for reconstruction. Under low-pressure conditions, there is no plastic connection between the deep and shallow caverns. Additionally, the plastic zone of the cavern covered with residue is relatively small, which indicates that the residue can inhibit the deformation and damage of the cavern wall's surrounding rock. Figure 13 shows the curves of the volume ratio of the plastic zone of the salt cavern's surrounding rock varying with time under different internal pressures. The volume ratio of the plastic zone is defined as the ratio of the total volume of the cavern's surrounding rock that enters the plastic state to the initial volume of the two cavities. From Figure 13, the volume of the plastic zone of the surrounding rock gradually increases with the operation time, but the increase rate gradually decreases, reflecting the characteristic that the unbalanced stress difference between the salt cavern and the surrounding rock gradually decreases and the whole system tends to be in equilibrium. Notably, the volume ratio of the plastic zone under an internal pressure of 10.5 MPa is 63% higher than that under 12 MPa. This clearly shows that the surrounding rock of the salt cavern is more likely to undergo plastic damage at lower pressures. These insights offer valuable guidance for designing the operating pressure of the salt cavern.  
Figure 11: Displacement–time curves of cavity roof under different internal pressures for deep and shallow cavities.
## 3 The 6G Technology
### Sixth-Generation Key Enabling Technologies
#### 3.4.1 High-Frequency Communications (Terahertz (THz) and mm Wave)  
THz ensures the availability of higher levels of wireless Gbps transmission rates, thereby guaranteeing improved broadband and increased data rate for effective 6G wireless communication [85; 86]. As provisions are being made for the 6G network, one of its many requirements is higher data rates and bandwidth. Consequently, the existing 5G setups such as the millimeter wave (mm Wave) (18-100 GHz) is further stretched to the micrometer wave (um Wave) (275 GHz-3 THz) in the 6G cellular network [85; 52]. THz bands, also referred to as submillimeter radiation, are carriers for cellular communications, ranging from 0.1 THz and 10 THz, and a corresponding wavelength of 0.03 mm-3 mm [63; 87]. Withinthe electromagnetic spectrum, the THz falls between the microwave and the infrared spectrum. Typically, frequency increase accounts for numerous advantages ranging from reduced beam width, improved positioning accuracy and angle estimation, exponential array gain, shorter wavelengths, smaller antennas, reduced interference, and better imaging capacity [52]. Studies have reported data rates of 18.3 Gbps for a THz network-based VR experience, with a reliability of 99.999% [85; 88]. Further improvement is being required to provide an even more effective data rate in the 6G network, and one such approach is by utilizing the parallelization of future micro-LED arrays [85].  
The transition to THz frequencies has proved to positively impact localization and sensing in the 6G network as the high-rate communication enhanced location sharing and sensing [52]. It is required to support the developing 6G services such as uMUB, uHSLLC, and uHDD [63]. Other practical applications of the THz include wireless cognition, imaging, communication, and precise positioning [86].  
Now, little progress has been made in antenna design and beamforming to implement effective THz bands. Also, development in supporting technologies to further support higher frequency bands is still needed [52].  
In high-frequency applications, selecting the optimal frequency for potential applications is often challenging, particularly for capturing radio channel characteristics [52]. Ray tracing simulation is one known approach to mitigating this problem, though it comes with a huge computational cost [52]. Consequently, alternative technologies such as ML integration are expected to play a key enabling role in improving the THz propagation challenges in future 6G networks [85].
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 9 Performance Analysis
### Land-Based UBRD System with Early Rail Defect Detection Capability: Performance Analysis
#### 9.4.3 Concluding Analysis on the Evolved UBRD System  
The cancellation of unwanted modes through phased array processing can be improved by using a larger number of transducers in the array employed for transmission and reception. The resulting system is able to accurately estimate the distance and direction of reflectors along the considered span of rails. However, since EOCs are not exclusively due to temperature variations, an important role in the achievable performance is played by techniques for compensating EOCs other than temperature. Defect detection has been performed through SVD and ICA; test results have led to the conclusion that ICA with dimension reduction performs better than SVD. Nevertheless, it has been found that the signature of a given defect spreads over different independent components; this might be due to the multi-modality of defect reflection signature. The last issue deserves further investigation.  
Based on the results obtained in Ref. [65], it can be stated that the detection of relatively small defects in the railhead is possible even if an array consisting of two transducers only is used and that, despite this positive result, the development of an automatic monitoring system, capable of autonomous operation with a low rate of false alarms, requires substantial effort. For this reason, the authors of that manuscript suggest that, during the design of an autonomous machine learning algorithm, the decision to stop trains or to ask for an on field inspection should be made by an expert able to interpret its output. A possible workflow for the development of a defect detection algorithm is also suggested: this could start from the analysis of the size of M-K scores, looking for values characterized by a significantly larger trend than the others. Then, it can be verified if the associated independent component resembles a single reflection from a defect. An appropriate level of alarm could be assigned on the basis of magnitude of the considered weight and its rate of increase. In any case, decision thresholds for these steps should be selected only after analyzing a large dataset.
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 4 Review of [PERSON]'s Scheme
### Drone Registration Phase  
The drone selects an \(ID_{D}\) and transmits it to the GCS via a secure channel. The GCS confirms the \(ID_{D}\) in its database. If the \(ID_{D}\) is in the database, the GCS transmits a message to the mobile user to choose a different ID; otherwise, it selects a random number (\(r_{D}\)), computes \(A_{D}=h(r_{D}\parallel ID_{D})\) and \(PK_{D}=h(ID_{D}\parallel k\parallel r_{D})\), stores \(\{ID_{D},A_{D},PK_{D}\}\) in its database, and transmits \(\{h(\cdot),PK_{D},A_{D},ID_{D}\}\) to the drone via a secure channel. The drone stores \(\{h(\cdot),PK_{D},A_{D},ID_{D}\}\) in its memory for subsequent use.
## 3 Proposed Technique
### Model for Energy  
A diagram illustrating the authors' energy model can be found in [24]. Equations (1) and (2) show the connection between the energy needed to send and receive a B-bit data packet (2). The receiver circuitry consumes a fixed amount of energy per bit (3).  
\[E_{Tx}(B,d)=\left\{\begin{array}{cc}&(E_{dec}+\varepsilon_{fs}d^{2})B&d<d_{0 }\\ &(E_{dec}+\varepsilon_{mp}d^{4})B&d\geq\ d_{0}\end{array}\right. \tag{1}\]  
\[E_{Rx}(B)=E_{Rx-elec}B \tag{2}\]  
\[E_{reng}(n)=E_{max}-E_{Tx}(B,d)-E_{Rx}(B) \tag{3}\]  
Several terms used in this model are defined in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Term** & **Meaning** \\ \hline \(E_{Tx}\) & Transmission node energy usage \\ \hline \(E_{Rx}\) & A node’s energy usage when receiving data \\ \hline \(E_{reng}\) & Remaining energy \\ \hline \(E_{elec}\) & Energy disbursed per bit by the transmitter circuitry \\ \hline \(E_{max}\) & Sensor node’s initial energy \\ \hline \(E_{Rx-elec}\) & Energy spent per bit of a receiver \\ \hline \(\varepsilon_{fs}\) & Energy required by radio frequency (RF) amplifier in free space \\ \hline \(\varepsilon_{mp}\) & Energy required by radio frequency (RF) amplifier in multi path \\ \hline \(d_{0}\) & Threshold distance \\ \hline \hline \end{tabular}
\end{table}
Table 2: Definition of terms used in the energy model.
## 7 Simulation Results
### Simulation Environment  
To evaluate the effectiveness of the considered cell search and frequency tracking methods, we carry out the in-band downlink NB-IoT system. There are two antenna ports at the transmitter and one antenna port at the receiver in in-band mode. As for the communication scenarios, the AWGN, ETU and EPA channels are investigated. Because NB-IoT terminals usually have low mobility, the Doppler drift is set as 1 Hz, which means that the coherence time is far greater than one radio frame. The crystal oscillator error is \(\pm 20\) ppm, so the frequency offset should be \(\pm 25.5\)\(\mathrm{KHz}\) under the assumption of 900 MHz carry frequency and \(\pm 7.5\)\(\mathrm{KHz}\) raster channel offset. The effective bandwidth and subcarrier spacing are 180\(\mathrm{KHz}\) and 15\(\mathrm{KHz}\), respectively. At the receiver, the processing frequency is 1.92\(\mathrm{MHz}\) and a 128-point FFT converts the time domain signals to frequency domain signals. Under this condition, \(N=128\) and \(N_{g}=10\) when the symbol index is 0 or 7, and \(N_{g}=9\) when the symbol index is another value. Moreover, since the NB-IoT terminals should achieve different coverage, we investigate the normal coverage for \(\mathrm{SNR}\leq-6\) dB and enhanced coverage for \(-15\)\(\mathrm{dB}\leq SNR\leq-6\) dB. When we verify the method of frequency tracking, the time window is set as UTG. Additionally, the proposed solution can also achieve continuous frequency tracking with slight modification.
# Automatic Detection of Tomato Diseases Using Deep Transfer Learning
## 2 Materials and Methods
### Convolutional Neural Network Models  
This research investigated the customization, retraining, and use of eleven deep learning CNN models to classify tomato diseases from leaf images. These models differ in the input size, structure, and computational efficiency of their internal operations, among other differences. However, the hyperparameters (e.g., number of iterations), which fine-tuned the training of these models, were unified throughout this work. The CNN models were: DarkNet-53 [24], DenseNet-201 [25], GoogLeNet [26], Inceptionv3 [27], MobileNetv2, ResNet-18, ResNet-50, ResNet-101 [28], ShuffleNet [29], SqueezeNet [30], and Xception [31].
# Microwave Staring Correlated Imaging Based on Unsteady Aerostat Platform
## 4 Simulation
### Effect of the Position and Angular-Measuring Accuracy on Imaging Performance  
This section is to study the effect of the measuring accuracy of position and attitude parameters on imaging. The imaging performance under different position accuracy and angular accuracy is simulated. In simulations, the measurement error is assumed to be independent and subject to Gauss distribution with zero mean and different variances. The smaller the variance, the higher the measuring accuracy. Figure 17 shows the imaging quality under different position-measuring accuracy and different angular-measuring accuracy, respectively. The results show that the imaging performance is very sensitive to the measuring accuracy of position and attitude parameters which means that the proposed method has a high demand of accurate measurement of position and attitude parameters.  
Figure 16: NMSE of the imaging results when only one rotation component exists at different amplitudes.  
Figure 15: The imaging results of UPIM when only one rotation component exists. (**a**) Only yaw angle, the NMSE is 0.27; (**b**) Only pitch angle, the NMSE is 0.28; (**c**) Only roll angle, the NMSE is 0.28.
# A Framework to Model Bursty Electronic Data Interchange Messages for Queueing Systems +
## 4 Discussion
### Modelling
#### 4.2.3 Message Interdependence  
We explored some aspects of interdependence via our classification model. For example, we found useful relationships between the ID and the XML document count. We checked for independence between the dependant messages and independent messages to see if they have a relationship with the tail of our service times. We used service times greater than 1 s as a starting cut off point. Our Fisher's exact test concluded that there is no dependence between these types of messages and the tail of the service times.  
We note that we observed correlations when modelling inter-arrival times from our paper [11]. This suggests that care might be required if applying a G/G/1 queueing model.
# Detection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm
## 2 Materials and Methods
### Proposed Method  
In this work, the proposed method is composed of two consecutive k-Nearest Neighbours (k-NN) to classify the mastitis. First, a k-NN model was used to classify data into healthy and mastitis. For those unhealthy data points, a second k-NN was used. The objective of this second kNN was to separate between degrees of mastitis.  
The following parameters can be identified for each of the two k-NN:  
* Preprocessing techniques used in the data.
* Wavelength selection threshold.
* Number of nearest neighbors.  
Each k-NN was independently optimized in two consecutive steps.  
In the first step, the spectral preprocessing technique was evaluated at different wavelength-selection thresholds, ranging from 1-100 according to the CFS. A grid search technique [49] was used to find the best technique for each k-NN model.  
In the second step, once the preprocessing technique was chosen, the model complexity was evaluated by a grid search which evaluates the number of neighbors between 1 and 10 and the threshold of CSF significance for the wavelengths from 1 to 100. Finally, the smallest neighborhood among the ones with the best performance was chosen, while the wavelength threshold was chosen according to the ones with the best accuracy.
## 3 Results and Discussion
### Comparison of SPME Fibers Using TgTPS1-\(\delta\)-Cadinene Synthase  
As an example of an sqTPS that produces a stable sesquiterpene we included _Tg_TPS1. _Tg_TPS1 was previously expressed and characterized in _S. cerevisiae_[7] and found to be a \(\delta\)-cadinene (**5**) synthase. The expression of _Tg_TPS1 in _N. benthamiana_ using PDMS and PDMS/CAR/DVB also resulted in the production of **5**, which was the major product detected, regardless of fiber used or injection port temperature (Figure 7). Minor products seen in the chromatograms could be due to rearrangements of uncharacterized minor biosynthetic products. Therefore, detection of **5** does not always point towards acid rearrangement but is also an enzymatic product itself.
## 2 Related Work  
One of the few pilot studies investigating the use of AR headsets in implant planning and navigation was published by [PERSON] et al., who, with the help of a modified commercially available AR headset, achieved a 1 mm distance and 3\({}^{\circ}\) angle deviation from preplanned implant positions [14]. [PERSON] et al. in their in vitro study compared information outputs from both headset- and monitor-based approaches, demonstrating similar levels of accuracy in angular- and distance-related implant measurements for each case [15]. [PERSON] et al. in their study achieved satisfactory results sufficient to implement in clinical practice with the use of a self-made augmented reality system [16]. The commercially available optical see-through system Magic Leap One (Magic Leap Inc., Miami, FL, USA) was used in [PERSON] et al.'s in vitro study, where the utilized system achieved results comparable to those obtained with the physical template guidance technique [17].  
While studies with HoloLens can be found in numerous medical fields, there is a noticeable lack of extensive research within the dental field, especially with the latest version of HoloLens. Existing studies employing HoloLens for the study of dental applications can be grouped into two categories: focused on training [18; 19] or monitoring [20; 21] and interaction based [22]. The first one focuses on displaying virtual content, while the latter involves the interaction between virtual content and real objects, which is the category with which guided implant placement studies are associated.  
One of the clinical studies by [PERSON] et al. successfully used HoloLens 1 to screen dynamic navigation system output in an AR headset, without the need for the surgeon to look at the system display [23]. [PERSON] et al. utilized HoloLens 1 in a mixed-reality-based dental implant navigation method and showed improvement over the hand-free approach [24]. In a recent study by [PERSON] et al., a comparison was made between the computer-guided dynamical navigational system and the use of HoloLens 2. The study indicated similar accuracy in implant distance-related measurements. However, it revealed that the AR system exhibited higher angular deviations (3.72\({}^{\circ}\)) compared to the conventional dynamic navigational system (3.1\({}^{\circ}\)) [25].