esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Performance Evaluation
### Simulation Results
#### 7.2.2 Impact of Sink Mobility  
In order to see the impact of sink mobility, we ran the simulations for different sink speeds (0 to 30m/s). In this experiment, the network consists of 8 mobile sinks and 400 sensor nodes. The number of sources does not change.  
Figure 12 shows the energy consumption as the sink speed changes. In both low and high speeds of the sinks, SCODE_plus_ shows that the total energy consumed is much less than SCODE, TTDD, and DD. The reason is because, aside from above reasons, SCODE_plus_ reduces the number of retransmissions of query and up dating sink's locations while the sinks are moving. The query only needs to resend as the sink moves to another cell. In contrast, TTDD and DD send more messages to propagate new location of the sinks throughout the sensor field to all sensor nodes.  
Figure 13 shows the delay of SCODE_plus_. Compared with the result in Figure 10, the average delay performance of SCODE_plus_ drops as the sinks are moving. This is because when the sinks move to another cell, they have to send a removal message to clear the old dissemination paths, and then resend a new query to establish a new one. Doing this causes more delay compared with the stationary scenarios. In Figure 14, as the sinks speed up, the average success ratio is always above 97%. This results show that SCODE_plus_ handles mobile sinks efficiently  
Figure 11: Success ratio vs. sink number.  
Figure 12: Energy consumption vs. sink speed.
## 3 Results
### NEGF Modeling of GFET  
To simulate devices, the graphene FET at IBM with gate length 40 nm is used [28], when the gate oxide is \(Si_{3}N_{4}\) with an oxide thickness of 10 nm. The pure graphene channel length is 40 nm, and the metals of drain and source are palladium. GFET potential values and corresponding transmission spectrum of the proposed method agree well with [PERSON]'s method [15], as shown in Figure 7. Figure 7 shows the transmission that is contributed by the thermionic current, the source-channel, and the channel-drain tunneling current. The transmission is non-zero for all energies, which shows the device is gapless, but there are minimum values that separate the regions in the transmission spectrum. According to \(I_{d}/V_{gs}\) and \(I_{d}/V_{ds}\) shown in Figure 8, for negative and positive \(V_{gs}\) Values the negative differential resistance (NDR) and positive resistance appeared, respectively. These effects in current/voltage characteristics are agreed well with other simulations [29; 30; 31] and experimental results [32; 33]. The above results show that the formalization and assumptions considered for GFET are acceptable.  
Figure 6: Band structure of **(a-c)** ZGNR, Nz = 3, 5, 14, and **(d–f)** AGNR, Na = 15, 4, and 8.  
Figure 7: GFET potential profile and corresponding transmission spectra of GFET; proposed method (solid line), and proposed approach (circles).
## 3 Methodology
### Formulation of Measurement
#### 3.3.2 Formulation of Baseline Difference Score  
In this section, we describe the method used to calculate the Baseline Difference Score (BDS), also referred to as the performance score in this study. The baseline is determined by averaging the bridge deck condition ratings for all bridges in Nebraska based on the deck age of each bridge. For this work, the terms performance score and BDS are used interchangeably. Further, we also compute the Nebraska average for each bridge deck with different surface-wearing types. Although the bridges are built for a life of 50 years, we continue to compute the baseline until the baseline reaches a mean condition rating of 4.  
The following is the overview for the computation of the BDS:  
* Compute mean condition rating using records available from Nebraska for each deck age.
* Compare each bridge time-series of condition rating data with baseline.
* The mean difference between the time-series baseline and the bridges is known as the BDS.  
In the following subsection, we describe the mathematical formulation of the baseline difference score [3].  
To calculate the BDS \(S_{b}\) for Bridge \(B\):  
Let  
\(i\) be the age of Bridge \(B\), when bridge \(B\) was first inspected.  
\(k\) be the age of Bridge \(B\), when bridge \(B\) was last inspected.  
\(\vec{C}\) be the vector of condition ratings of the Bridge \(B\) from age \(i\) to age \(k\).  
\(\vec{X}\) be the average condition rating of all bridges from age 1 to 60.  
\(X_{y}\) is the vector of the average condition rating of all bridges from age \(i\) to age \(j\), such that \(j\leq k\).  
\(D_{b}\) be the vector of differences computed from \(\vec{C}\) and \(X_{y}\), such that \(j\leq k\).  
\[D_{b}=\vec{C}-\vec{X_{y}} \tag{2}\]  
Then, BDS \(S_{b}\) is computed as in Equation (3):  
\[S_{b}=\overline{D_{b}} \tag{3}\]  
From the formulation above, a BDS is computed for a bridge using a time series of its condition ratings available in the NBI. This computational strategy limits the risk of auto-correlation. In contrast to condition ratings of a particular bridge that is ordinal from 0 to 9 values, the baseline is continuous, as it is the mean condition rating over all bridges in Nebraska at a particular age. For the deck baseline, we compute the baseline using the computed deck age instead of a bridge's built age. We also observe that the bridge condition ratings may fluctuate above and below the baseline because of improvements from interventions and deterioration of the bridge deck components. Thus, bridges with equal baseline difference scores may have highly varied condition ratings.
# Dynamic Simulation Model-Driven Fault Diagnosis Method for Bearing under Missing Fault-Type Samples
## 5 Conclusions  
In this study, a new method based on dynamic simulation model and WGAN-GN is proposed as a solution for fault diagnosis of missing experimental fault samples of bearing. In the proposed method, a dynamic simulation model is first built to obtain simulated data of missing fault type, which can be used to form relatively complete fault samples with other signals. Then WGAN-GN is employed to generate expanded samples from a smaller number of relatively complete fault samples. Next, these simulated samples are combined with the original samples to form an enhanced fault dataset which can be called as complete fault dataset. Finally, SAE is used to classify the complete fault samples. Three datasets of rotating machines were used to confirm the validity of the method. The following is a summary of the main conclusions:  
1. It is demonstrated that the developed dynamic simulation model can generate high-quality replacement samples with missing fault samples to some extent.
2. The effective feature extraction and data generation capability of the proposed model is illustrated by the features learned continuously from the hidden layer of WGAN-GN.
3. The experimental results show that using the proposed method can help to improve the accuracy of diagnosis when the types of fault sample data are insufficient.
4. Both the applicability to other mechanisms and the problem regarding the in-fluence of noise are part of our future research objectives.  
Conceptualization, J.M.; methodology, X.J.; software; J.W.: writing--review and editing, B.H.; funding acquisition, X.J.; supervision, Z.Z.; formal analysis, H.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the [China Postdoctoral Science Foundation] grant number [2021M70275, 2022T150552], [Prospective Application Research of Suzhou] grant number [SYG202111], [National Natural Science Foundation of China] grant number [52005303, 52105110], [Natural Science Foundation of Shandong Province] grant number [ZR2022 ME119, ZR2020 QE157, ZR2021 QE024].  
Not applicable.  
The experimental data used in this paper is from Western Reserve University, which can be found at the following website: [[http://www.52](http://www.52) phm.cn/blog/detail/54]([http://www.52](http://www.52) phm.cn/blog/detail/54) (accessed on 4 December 2021).  
The authors are very grateful for all constructive comments that helped us improve the original version of the manuscript.  
The authors declare no conflict of interest.  
Figure 16: Dimensionality reduction visualization results on Dataset C: (**a**) GAN. (**b**) WGAN. (**c**) WGAN-GN.
# Energy Efficient Routing Protocol in Sensor Networks Using Genetic Algorithm
## 4 Methodology
In Algorithm 1, E is the efficient routes array produced by the survivor selection process. The output routes generated by both crossover and mutation phases are C and M, respectively. POP_F is the population of the routes generated by AOMDV out of the PopSize where the elitism method is applied. FM, FP, and FO are the fitness scores F for the following: the child route generated by the mutation M process and the parents P and O generated by the AOMDV mechanism and/or crossover process. We summarize the steps of the GA-AOMDV algorithm as:  
1. AOMDV protocol returns an array of routes (PopSize) based on the minimum number of hops (Genes). PopSize is the set of the parent routes where the GA will be applied to generate new child routes as explained below,
2. FF based on the minimum consumed energy of nodes involved in the PopSize is evaluated. Using the elitism method, routes with low fitness levels are dropped and only consider remaining routes as parents to be used in the GA,
3. Employ the crossover process over the elitism parent routes to generate new child routes through using P\({}_{\rm c}\). 4. Employ mutation process on the parent and child routes using P\({}_{\rm m}\),
5. Employ the FF again on every child route that can be considered as a potential route if its fitness score is higher than its parent routes fitness and this is the SurvivorSel in this step,
6. Store all the potential parent and child routes in an efficient routes array E,  
Figure 2: GA-AOMDV flowchart. Sort entries of array E in descending manner where the efficient route is the one with the highest fitness. Other routes in E will be utilized when the selected route fails because of channel disconnection or faulty nodes. To better understand the algorithm, we consider the network shown in Figure 3 We assume the source node is S and the destination node is D. S will go through the route discovery phase using the AOMDV method. AOMDV will not select the route (S, H, I, J, K, D) because it does not have the least number of hops. AOMDV returns three routes including R1, R2, and R3 who have the minimum number of hops:  
R1: S, C, E, F, D  
R2: S, B, E, A, D  
R3: S, G, E, A, D  
Figure 3: Network topology. We assume that the FF scores are 9.4, 9.1 and 4.7 for R1, R2, and R3, respectively. Using the elitism method, R3 will be dropped and only R1 and R2 will be considered for the GA application. Based on the crossover and mutation process, generated offspring are as follows:  
R4: S, C, E, A, D  
R5: S, B, E, F, D  
The offspring will be checked by the FF evaluation where their scores are 9.6 and 9.8 for R4 and R5, respectively, so they will be added to the population. R5 has the highest score and this will be selected as the optimum route. If any failure occurred to R5 links or nodes during the data communication, R4 will replace R5.
## 3 Metasurfaces
### Definition and Salient Characteristics of Metasurfaces
In [34], two-layered metasurfaces were used to eliminate the reflection losses; the top layer was employed to temper the electric polarizability while the bottom layer was used to control the magnetic polarizability. An entirely different paradigm to structure metasurfaces is presented in [36; 37]. Highly absorptive dielectrics and metals have been employed to construct a sub-wavelength thin metasurface. This metasurface achieves the desired phase shift over a nano-scale and is made with highly absorptive dielectrics and metals with finite conductivity. Since the reflection from the interface is no longer perfect, the phase can be tailored and desired phase shifts can be achieved over a much smaller distance. This is in contrast to traditional optical layers which employed extremely low loss metals and dielectrics, and exploit Fabry-Perot type interference for the phase shifts. However, unlike metasurfaces, the thickness of such (traditional) films is comparable to the operating wavelengths [37].
## 4 Matrix Converters in the Connection of Renewable Energies to the Grid
### Matrix Converter Applications for Integration of Photovoltaic (PV) Systems
#### 4.1.3 Indirect Matrix Converter Topology Application  
Instead of using a single shunt converter or the most common VSIs for the shunt and series connections to the grid, it is also possible to connect the PV array to the SG using an IMC [67; 71], as shown in Figure 12. The PV array is inserted on the virtual 'dc link' of the IMC and the system overall guarantees improved compactness and higher reliability, due to the absence of a bulky electrolytic capacitor in the dc link [72; 73; 74; 75]. The series voltages \(v_{\text{ABC}}\) compensate the grid voltages \(v_{\text{abc}}\) to supply sinusoidal voltages to the sensitive non-linear loads while the shunt converter currents \(i^{\prime}_{\text{abc}}\) compensate the load currents to absorb sinusoidal currents to the SG.  
In addition, the matrix converter technology offers higher power density with bidirectional power flow and high-quality input and output currents [72; 76; 77]. Even though it is not commonly used for general-purpose applications as it lacks the voltage-step-up capability (input/output ratio of 0.86 for sinusoidal modulation) [76] and has no internal energy storage, the topology in Figure 12 is suited to the PV array connection considering that the PV array voltage is usually lower than the grid voltage. The shunt converter, therefore, acts as a voltage-step-up CSI with a minimum transfer ratio of 1.155, which defines the maximum PV array operating voltage [54].  
Due to the direct connection between the shunt and series converters, the switching vectors must be selected carefully, to avoid applying negative voltage to the 'DC link'. Indirect space vector pulse width modulation (SV-PWM) [67] or direct sliding mode control [71] can be applied to the converters to control the voltages at the PCC and the grid currents.  
Figure 11: Proposed grid-tied photovoltaic (PV) array with a current source inverter (CSI).
## 5 Materials and Methods
### LC-MS/MS Analysis of Cannabis GT Proteome  
Proteomic analysis of trichome-derived peptides was as described in [PERSON] et al. [33] and conducted on an Ekspert nano LC400 uHPLC system (SCIEX, Framingham, MA, USA), interfaced with a TripleTOF 6600 QTOF mass spectrometer (SCIEX, Canada), which was equipped with a PicoView nanospray source (New Objective, Littleton, MA, USA). Separation of peptides was facilitated by a dual-column setup, employing a 5 mm \(\times\) 300 mm C18 3 \(\upmu\)m trap column (SGE Analytical Science, Ringwood, VIC, Australia) and a 75 \(\upmu\)m \(\times\) 150 mm ChromXP C18 CL 3 \(\upmu\)m analytical column (SCIEX, Canada). The elution gradient was formulated with 0.1% formic acid in both water (Solvent A) and acetonitrile (Solvent B), increasing Solvent B from 2% to 40% over 60 min, followed by a ramp to 90% for 5 min, and maintained at 90% for another 5 min before re-equilibrating to 2%. The flow rate of the mobile phase was maintained at 400 nL/min, and the column was thermostatically controlled at 45 \({}^{\circ}\)C. Sample peptides, in 5 \(\upmu\)L aliquots, were introduced to the trap column at a flow rate of 10 \(\upmu\)L/min for 5 min, then transferred to the analytical column at a consistent flow rate. The SWATH acquisition mode was employed for MS/MS analysis, capturing 100 product ion spectra per cycle across the range of \(m/z\) 350 to 1500, with each product ion window defined at 6 Da. Variable collision energies were applied, ranging from 16 to 60 V, with an energy spread of 5 V. The TOF-MS scans were conducted with an acquisition time of 50 ms, and each product ion scan was allotted 25 ms.  
Data collection and processing were executed using Analyst TF software version 1.7 (SCIEX). The SWATH-MS data underwent spectral alignment and targeted data extraction using the SWATH Processing Micro App within PeakView software (Version 1.2, SCIEX), leveraging an inhouse assembled spectral library built from Hindu Kush spectral data [33]. The extraction parameters were set with a 15 min window and included ten peptides per protein, five transitions per peptide, a peptide confidence threshold of greater than 95%, exclusion of shared peptides, and an extracted ion chromatogram width of 75 ppm.
## 2 Related Work
### Algorithms for Map Generation
The scheme consists of three algorithms to build contour maps: distributed spatial and temporal data suppression, centralised contour reconstruction via interpolation and smoothing, and a mechanism for multiple hop routing. As in our approach, the interpolation algorithm utilises knowledge of the terrain of the sensor field and the location of the sensors to accurately reconstruct the contour maps. This scheme is based on the dumb sensor, smart sink paradigm, which imposes little processing and storage overhead on nodes. Therefore, it is not suitable for the delivery of field information visualisations. However, in this paper we are interested in in-network data processing to produce low cost maps. Nevertheless, our algorithm targets networks with limited capabilities, i.e., sensors have limited storage space and computational power. In the lack of recent applicable approaches to compare the performance of our work against, we choose the suppression-based mapping. To address the inherent limitations of aggregation based methods, [PERSON] and [PERSON] [15] proposed a method called _Iso-Map_ that intelligently selects a small portion of the nodes, _isoline nodes_, to generate and report mapping data. Partial utilisation of the network data leads to a decrease in the mapping fidelity and isoline nodes will suffer from heavy computation and communication load. Furthermore, the location of the mapping nodes can also affect the directions of traffic flow and thereby have a significant impact of the network lifetime. Finally, in sparsely deployed low density networks it is difficult to construct contour maps based only on isoline nodes. The positions of isoline nodes provide only discrete _iso-positions_, which does not define how to deduce how the isolines pass through these positions. To conclude, mapping is often employed in WSN applications, but as yet there is no clear definition (or published work towards) a distributed mapping framework that would aid the development of more sophisticated services. The development and analysis of such a framework is the key novel contribution of the work presented here.
## 3 Security Attacks on AV Sensors: A Closer Examination of GPS Systems  
Autonomous vehicles (AVs) substantially draw upon sensor systems for navigation and critical decision-making. Among these, the global positioning system (GPS) holds notable significance, providing precise geolocation, speed, and temporal data, regardless of the vehicle's global position and meteorological conditions. Nevertheless, as with any technologically sophisticated system, GPS is not immune to potential security compromises. Security compromises generally manifest in various types, classified as:  
1. **Spoofing Attacks**: In such instances, adversaries generate synthetic signals to mimic legitimate GPS signals. These deceptive signals, once received by the AV, are mistaken as authentic, thereby misleading the AV into calculating its location inaccurately.
2. **Jamming Attacks**: During these attacks, the perpetrator utilizes devices that transmit signals matching the frequency of the GPS signals, with the intent to overwhelm the authentic signals. This interference inhibits the GPS receiver's ability to establish a connection with the original GPS signals, effectively impeding its geolocation determination.
3. **Meaconing Attacks**: These attacks entail the interception of GPS signals, which are then deliberately delayed before retransmission. This activity can cause the GPS receiver to miscalculate its position.
4. **Replay Attacks**: These consist of capturing GPS signals and retransmitting them at an alternative time or location. This action can mislead the GPS receiver into miscomputing its position or time.  
Each of these attacks has severe implications for AVs, potentially leading to navigational inaccuracies or even disastrous vehicular collisions. Thus, the implementation of robust security countermeasures to protect GPS systems against these breaches becomes an academic and industrial priority.
# Assessment of Diagnostic Radiology Facilities Technical Radiation Protection Requirements in KSA
## 2 Methods
Based on the national and international standards, the dose limit is 20 mSv per year for radiation workers and 1 mSv per year for the public in the kingdom. The qualification requirements of the radiation workers, the license for radiation safety officer (RSO), the safety requirements, the shielding requirements for the X-ray room, and the acceptance test requirements for the X-ray machine itself are covered in that guide. Based on the data published in the KSA-SFDA report in 2015, there were 109 diagnostic X-ray facilities in different KSA governorates that were evaluated and analyzed from the viewpoint of radiation protection requirements and national regulations. The inspection was carried out by the team inspector from KSA-SFDA who is authorized to work within the powers described in the regulations to monitor compliance with the regulations. The planning inspection strategy framework of the activity carried out by a team of staff from KSA-SFDA covered the following topics to ensure that:  
1. The maintenance program for the used equipment is properly maintained;
2. The X-ray room and machine are all licensed by the regulatory body;
3. Evaluation of the design structure layout (X-ray room) from the viewpoint of radiation safety and security;
4. The degree of qualification, knowledge, licensee, the level of training courses given to the workers, and the total number of the radiation workers in every facility;
5. The records for the radiation survey program;
6. The radiation warning signs should be clear to the user and the public and prominently displayed and the interlock system for the door of the X-ray room is functioning. 7. Ensuring that the radiation safety monitoring program existed at the facility;
8. Ensuring that the radiological emergency plan existed at each facility for any incident action updated periodically;  
Figure 1: The location and the number of examined diagnostic X-ray facilities selected in the site visit at KSA governorates. The radiation level outside the X-ray room should not be more than 10 uSv/h during the exposure time. * Ensuring that any X-ray room is shielded well against any leakage from the ionizing radiation devices;
* Review the shielding design of the entrance door, the patient window, the operator shielding and the shielding tools for the X-ray room. Table 1 shows that, the design layout for the facility including the radiation shielding, radiation protection requirements, and radiation safety measures at those facilities [12; 17]. Figure 1, shows, the number of the examined diagnostic X-ray facilities covered in the inspection activities at different KSA governorates (35 governorates) during the site visit of the SFDA staff. From Figure 1, the majority of the X-ray machine is located in the four KSA big cities, which are Jeddah, Riyadh, Al-Ahsa'a, and El-Medina due to the high percentage of the population in those cities.