esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The effective stress intensity factors (SIFs) were derived by an inverse method using the relative displacements of the measured crack face. They were found to be 10%-70% lower than the nominal SIF, and a reasonable correlation of the measured FCG rates could be made using the effective SIF. [PERSON] and [PERSON] [11,12] conducted fatigue tests to determine the coplanar FCG rate on both rail and wheel steel under non-proportional mixed mode I/III loading cycles. They could induce long coplanar cracks under this condition. Moreover, the cracks tended to branch when the degree of overlap (\(\delta\)) between the mode I and III cycles increased. As mentioned above, the previous studies about FCG under non-proportional mixed mode loading cycles were apparently focused on the mode I/II loading and coplanar growth in rail steel. Only a detailed study on coplanar and branch FCG under non-proportional mixed mode I/III loading cycles has been performed, by [PERSON] and [PERSON] [11,12], for rail and wheel steel. However, the elucidation of the phenomena inherent to FCG under non-proportional mixed mode I/III loading is not sufficient in their studies. Therefore, in this study, the FCG data obtained from non-proportional mode I/III loading cycles were re-constructed by using novel and reliable equivalent SIF ranges to obtain good correlations. In addition, a finite element analysis (FEA) was performed thoroughly to investigate the crack behavior under these loading cycles. This paper, which is part 2 of two companion papers, presents FCG under non-proportional mixed mode I/III loading and is organized as follows. Section 2 describes the detailed experimental methods and presents the experimental results. In Section 3, the FEA model for predicting the crack path direction is presented, and the results are provided. Section 4 provides detailed considerations and a discussion by comparing the experimental and FEA results. Finally, the results obtained herein are summarized in Section 5.
# Ultra-Small Fiber Bragg Grating Accelerometer
## 1 Introduction  
Usually, there is an inertial object and a sensing element inside an accelerometer. The inertial object is held by a vibration arm (a stick or string, etc.) to vibrate around its equilibrium position. The sensing element is attached to the vibration arm, or is part of the vibration arm. When the inertial object vibrates, it pulls the vibration arm and sensing element. Usually, the working frequency range of an accelerometer is up to half of its resonant frequency, and the sensitivity of the accelerometer changes significantly above this frequency range [1].  
The improvement of the sensitivity of an accelerometer can be obtained by using a heavier inertial object. However, this method will reduce the resonant frequency. By contrast, the improvement of the resonant frequency of an accelerometer can be obtained by shortening the vibration arm, without reducing the sensitivity. Therefore, it is optimum for the vibration arm to be as short as possible, provided that its inertial object is adequately heavy.  
Fiber Bragg grating (FBG) has many intrinsic advantages, such as immunity to electromagnetic interference, electrical isolation, frequency modulation, and ease of multiplexing [2]. Many different FBG accelerometers have been developed [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. Some FBG accelerometers used a springy stick to hold the inertial object at the equilibrium, and the FBG was attached to the springy stick [3; 4; 5; 6]. When the inertial object vibrated, the springy stick and FBG was pulled. Some used a springy joint to hold the inertial object at the equilibrium, and the FBG was pulled when the inertial object vibrated [7; 8; 9; 10; 11]. The 10-mm vibration arm was the shortest among them, and the sensitivity and resonant frequency were 19 pm/g and 3000 Hz, respectively [9]. Some used optical fiber containing Bragg grating as the vibration arm, and usually showed a higher sensitivity and smaller size than other types [12; 13; 14; 15].  
Here, by using a 7-mm-long optical fiber containing Bragg grating as the vibration arm, we experimentally demonstrate an FBG accelerometer with the shortest vibration arm among similar structures, as far as we know. Table 1 compares such FBG accelerometers [12; 13; 14; 15]. They are listed in order of the lengths of their vibration arms.
# Medical Augmented-Reality Visualizer for Surgical Training and Education in Medicine
## 2 Materials and Methods
### Contour-Based Shape Descriptor  
The position and orientation of the liver phantom are calculated from its contour. The initial contour is a vector comprising 2D points represented in a Cartesian coordinate system (\(XY\) plane). The contour may be obtained by either using the computer-generated image of a projection of the 3D model onto a 2D plane of the virtual camera or analyzing video footage. The proposed CSD is invariant to object scale and rotation about the camera axis and is represented using polar coordinates with the pole located at the geometric center of mass of the 2D object silhouette (Figure 2). The polar axis is defined by the contour start point, which is selected using two points (\(P_{\text{CP1}}\), \(P_{\text{CP2}}\)) at the intersection between the contour and the best-fitting line (\(L_{\text{BFL}}\)) of all the contour points. Then, the contour start point is selected as the farthest point from the pole (Figure 3). The scale-invariant feature is achieved by normalizing the distances in terms of the distance from the pole to the contour start point. The rotation-invariant feature is achieved by using line \(L_{\text{BFL}}\) and the contour start point. The CSD is obtained by uniform angular sampling (every \(6^{\circ}\)) of the contour and storing the parameter pair (\(\alpha,d\)) per point, where \(\alpha\) is the polar angle and \(d\) is the normalized radius of the contour point.  
Figure 1: MARVIS system (**top**) and subsystem flowchart (**bottom**).
## 3 Current Status of the Circuit Component in the MMW Regime
### Other MMW Control Circuits  
#### 3.6.1 Switch in Si-Based Technology  
MMW switches, which include SPST switches and SPDT switches [178, 179, 180, 181, 182, 183, 184], are essential for a MMW communication system. For a MMW switch, most important design considerations are low insertion loss and high isolation. However, in Si-based technologies, higher loss and parasitics are still challenges instead of their high-level integration and low cost natural. In MMW switch design, the shunt-series and traveling wave (transmission line integrated) techniques are popular.  
The series-shunt technique offers appropriate performance of single port single throw (SPST) switches in MMW [178]. As for the single port double throw (SPDT) switch design; the drain-to-source capacitance has great influence on the transistor performance both in the series and parallel path. In [182], the effect of the parasitic capacitance can be reduced by the LC-tuned technique for a narrow band CMOS SPDT switch design.  
The transmission-line integrated switches are promising in MMW frequency due to good performances [180, 183]. Also, in [179], a 90 nm CMOS switch exploits traveling wave concept to exhibit a wideband performance in 50-94 GHz. It is worth mentioning that a filter-integrated switch concept is proposed with better sideband rejection for the undesired bands [185, 186] in GaAs HEMTs. The body-floating technique was reported to increase the power handling capacity of CMOS switches [187].  
In [188], a 60 to 110 GHz SPDT switch with traveling wave concept and body bias technique is implemented in the 90 nm CMOS technology. It has a measured insertion loss of 3-4 dB and a measured isolation better than 25 dB. In [189], a low insertion loss and high isolation 90 nm CMOS switch is implemented. In order to achieve a low insertion loss and high Tx-to-Rx isolation, the double-shunt switch on the transmitter side and the matching-network switch on the receiver side are adopted. The switch exhibits a measured insertion loss and Tx-to-Rx isolation of 1.9 and 39 dB, respectively, at 60 GHz.  
Figure 17: Phase noise of the recent published MMW SiGe VCOs. Data is taken from [173, 174, 175, 176, 177].
# Distributed Reputation for Accurate Vehicle Misbehavior Reporting (DRAMBR)
## 4 System Model
### Vehicle Behavior in OE and OR  
Vehicles can act very differently on a network depending on their intentions and strategy. While an honest vehicle always follows protocols and truthfully reports, malicious ones may exhibit dynamic behavior, for instance, by deliberately causing harm in the system, such as sending misleading information or behaving strategically honestly with the intent of gaining confidence to get undetected, their actions will be challenging to anticipate and control. Generally, trust and reputation-based systems are exposed to two main behaviors listed below:  
* **Honest**: 1--In V2V, create, broadcast, or forward correct messages \(M_{\mathit{sgs}}\) (OE Phase). 2--In V2I, create and submit correct MRs (OR Phase).
* **Malicious**: 1--In V2V, ignore the correct messages \(M_{\mathit{sgs}}\) or create and broadcast false \(M_{\mathit{sgs}}\) (OE Phase). 2--In V2I, create and submit false MRs (OR Phase).  
The system makes a distinction between intentional and unintentional misbehavior, see Figure 6, with the latter encompassing all vehicle faults and error scenarios. While we take into account all these considerations, the primary focus is on the accuracy of the MR submission and the behavior of the reporters. Specifically, we analyze the false MR (\(MR_{f}\)) causes and how the reporter's reliability impacts the overall trustworthiness of the system.  
A significant threat scenario arises by an MR Attack (\(MR_{f}\)Reporter), when the attacker with high RV manipulates the reporting system by not reporting the misbehavior and generates a \(MR_{f}\) for an honest vehicle, as illustrated in Figure 7. This type of activity is similar to the badmouthing attack [40], which might result in assigning a high RV to a vehicle that deserves a lower one and a low RV to one that deserves a higher one. This tactic inflates the reputation of certain vehicles, making them seem more trustworthy while deflating the reputation of others and harming their credibility. Such rating manipulation distorts the accurate feedback, misleads other vehicles, and undermines trust in the platform's rating system.  
The \(MR_{\mathit{Attack}}\) in Algorithm 2 shows the attack activity considered in this study. Assuming \(V_{i}\) is behaving honestly, the attacker targets \(V_{j}\) by generating a false misbehavior report (\(MR_{f}\)). The (\(MR_{f}\)) is generated only if the status of the target vehicle is not already flagged as misbehaving (\(S(V_{j})\
eq\) misbehaving). However, if the target vehicle is already flagged as misbehaving, no MR is sent, and the attack halts.  
To mitigate such an attack, our system follows a thorough process of comparison and aggregation, ensuring a more accurate evaluation as illustrated in the following sections.  
Figure 6: False \(M_{\mathit{sg}}/MR_{f}\) causes.
## 3 Results and Discussion
### Optimization of the W2 WQMP - Results and Discussion
The recommendations for the W2 WQMP are:  
* Maintain the global WQMP of W2 according to the recommendations presented in Figure 10B,
* Collect samples in 50% of rain events (modality 3),
* Obtain at least 10 samples for each of these stations per year,
* Respond to the actionable tasks in Table 2, with the priority of adding an integrative (and affordable) parameter such as specific conductivity to respond to the knowledge needs on road salts and other contaminations and identify other parameters that can be financed (the WQMP can still be pursued while these tasks are completed),
* Consider adding rotating WQMPs for the subwatersheds (more stations, more water quality parameters and a higher sampling frequency. Through two different types of analysis (Kriging and Moran) proposed in the literature [5; 6] to evaluate a river station network, it was possible to suggest stations to be retained or removed for the W2 WQMP. The IDSS was then solicited to submit the suggested stations to another series of questions on the sampling stations, such as sampling site justification, specific monitoring objectives for these sites, etc. Submitting the results of this analysis to further decision resulted in a final set of decisions to make, without consulting the manager of the WQMP. Actually, the Kriging and Moran analysis requires quite some level of expertise in statistical analysis [7]. When an expert in statistics is not available, the watershed manager who uses the IDSS could skip the analysis and be guided through a series of questions on the IDSS and still make a valid decision, even if not as well documented as the one made available through the process of using statistics. The IDSS was based on expert input on the type of questions and decisions which have to be made during the planning, management, and optimization of a WQMP. Regardless of the guideline, handbook or method based on geographical information systems, statistical analysis methods, etc. used, there is still a need for expert input. In order to standardize and capture the expert input, the type of expert input required for these decisions was integrated into the IDSS. The optimization of the two case-study WQMPs showed that expert input was required and that it was also possible to use the IDSS instead of this expert input, as it is not always certain that the knowledge on these issues could be adequately transferred within the organization or to these organizations. The advantage of using the IDSS is that there are some standardization and documentation possible for this expert input. As the system was designed to evolve, it is possible to add additional questions and decision problems to the system. The field of WQMPs is evolving rapidly and decisions must be made on new types of water quality analysis strategies (e.g., continuous monitoring devices and their challenges of positioning the apparatus, cleaning the data, etc. ).
# Motion Deblurring for Single-Pixel Spatial Frequency Domain Imaging
###### Abstract  
The single-pixel imaging technique is applied to spatial frequency domain imaging (SFDI) to bring significant performance advantages in band extension and sensitivity enhancement. However, the large number of samplings required can cause severe quality degradations in the measured image when imaging a moving target. This work presents a novel method of motion deblurring for single-pixel SFDI. In this method, the Fourier coefficients of the reflected image are measured by the Fourier single-pixel imaging technique. On this basis, a motion-degradation-model-based compensation, which is derived by the phase-shift and frequency-shift properties of Fourier transform, is adopted to eliminate the effects of target displacements on the measurements. The target displacements required in the method are obtained using a fast motion estimation approach. A series of numerical and experimental validations show that the proposed method can effectively deblur the moving targets and accordingly improves the accuracy of the extracted optical properties, rendering it a potentially powerful way of broadening the clinical application of single-pixel SFDI.  
spatial frequency domain imaging; motion deblurring; single-pixel imaging +
Footnote †: journal: Applied Sciences
## 6 Simulated Results
### Energy Consumption  
Figure 5 demonstrates the evaluation of energy consumption of the EGCIR protocol in the comparison of other solutions under a varying number of nodes. The energy consumption is defined as the fraction of depleted energy resources among sensor nodes while transmitting, aggregating and receiving the network data. It is observed that the EGCIR protocol significantly reduces a load of energy consumption over the network field by an average of 9% and 17%. This is because it exploits a centralized cluster formation and cluster head selection process. All information of the entire network field is placed on a centralized location at the BS, which is based on the updated node conditions. Furthermore, cluster heads only change their positions when there is a demand of member nodes instead of the regular epoch. Unlike other solutions that impose extra energy load on the sensors to protect data packets from malicious nodes, the EGCIR protocol securely forwards the communication keys between both the source and next-hop cluster heads using a light computational XoR mathematical function. Furthermore, the existing solutions deplete additional energy constraints on transmitted more control messages for the formation of routing paths. On the other hand, the EGCIR protocol using graph-based approach for the formation of cluster heads linkage consumes minor energy in the process of route management.
# Research on a Rail Defect Location Method Based on a Single Mode Extraction Algorithm
## 2 An Accurate Modal Identification Method
### Excitation Response Analysis of Rails  
On the basis of the excitation response analysis method, the vibration signals of a non-defective rail can be calculated at any point. Firstly, the system function model \(U\) of the rail should be obtained. Then, the frequency domain signal \(\widehat{F}(f)\) of the excitation function signal \(v_{1}(t)\) can be obtained.Thereafter, the excitation response result can be calculated \(V=U\times\widehat{F}(f)\). Finally, the inverse Fourier transform of \(V\) can be determined, which is the excitation response of the time domain vibration signal.  
The system function model of the rail [21] is shown in Equation (6), where \(m\) is the mode number (\(m=1,2,\ldots,M\)). \(\Omega_{m}\) is the amplitude and phase parameters of mode \(m\). \(U_{m}^{L}\) and \(U_{m}^{R}\) are the left eigenvector and the right eigenvector of mode \(m\). \(\widetilde{p}\) is the amplitude of the excitation signal. \(x_{s}\) and \(x\) are the longitudinal coordinates of the excitation point and the receiving point, and \(U_{m}^{Rup}\) is the mode shape of mode \(m\).  
\[U(y,z,f)=\sum_{m=1}^{M}\Omega_{m}U_{m}^{Rup}e^{-i[\xi_{m}(x-x_{s})]} \tag{6}\]  
Figure 3: (**a**) Phase velocity; and (**b**) group velocity dispersion curves.  
In this form, the parameters are expressed as follow:  
\[\Omega_{m}=-\frac{U_{m}^{L}\widetilde{p}}{B_{m}}\] \[B_{m}=U_{m}^{L}BU_{m}^{R}\] \[B=\begin{bmatrix}K_{1}-\omega^{2}M&0\\ 0&-K_{3}\end{bmatrix}\]  
The result of the frequency response analysis of the excitation response \(V(f)\) can be expressed by Equation (7).  
\[V(y,z,f) =\widehat{F}(f)\cdot U(y,z,f)\] \[=\widehat{F}(f)\cdot\sum_{m=1}^{M}\Omega_{m}U_{m}^{Rup}e^{-i[ \widehat{s}_{m}(x-x_{x})]} \tag{7}\]  
The inverse Fourier transform result is the time domain vibration signal of the node.
# Performance of Payement Temperature Prediction Models
## 4 Discussion
### Effect of Geographical Location  
The Viljoen model performed best followed by the Diefenderfer model and then the SHRP Superpave model for the predicted maximum temperature in asphalt at the two locations (Akumadan and Sogakope) in Ghana. It was noted from the literature review that the prediction accuracy of the models is highly dependent on the geographical location. The Viljoen model was developed in South Africa and the temperature data of the materials were also sourced from South Africa. In addition, the effect of geographical location has been tested with data sourced from Ghana, on the African continent. Thus, it would be necessary to test the models with data sourced from other continents. There is also a need to have locally developed models for use in pavement design.
## 1 Introduction  
The aviation industry is the most modernized transportation industry and has become essential for the economy. However, safe operations are always critical, as accidents during flights cause serious damage. Thus, the development of the aviation industry is closely related to that of inspection and maintenance technology. Therefore, it is necessary to train personnel for inspection and maintenance with appropriate tools, and the training methods need to be improved with the intensive development of science and technology. However, the cost of training with adequate facilities is significantly high and also requires a high level of safety. Along with this, a variety of engines for various aircraft require even higher costs for training. Therefore, it becomes critical to find a new approach to replace and improve the traditional training methods.  
Virtual reality (VR) is one of the prominent technologies in the era of Industrial Revolution 4.0. As an advanced technology that allows experiences in the virtual and 3-dimensional environment, VR has three important aspects: immersion, interactivity, and imagination [1]. Recently, VR has been widely used in the fields of medical science, military, culture, tourism, and scientific research. VR applications are used for entertainment and education, as well as inspection and maintenance in the design and manufacturing industry.  
In this study, we developed an aircraft maintenance virtual reality (AMVR) system for training students in the aviation industry and evaluated the effectiveness of the system. Focusing on the inspection of the aircraft fuel system of the Dornier-228 aircraft, we created three scenarios of inspection and maintenance, including walk-around inspection, disassembling, and re-assembling of the horizontal stabilizer of the aircraft. The developed system provides an effective way to train students with lower costs and higher efficiency.