esa-sceva/satcom-chunk-collection huggingface dataset:
# A Bayesian Control Chart for Monitoring Process Variance
## 7 Conclusions  
In this paper, we propose a new control chart, the Bayesian EWMA variance chart, to monitor changes in the process variance when the distribution of a critical quality characteristic is non-normal, unknown, or skewness-time-varying. The average run lengths (ARLs) of the proposed chart are calculated and show that the chart performs well. When compared with the well-performing SS EWMA-V chart, the out-of-control detection performance of the proposed Bayesian EWMA variance chart is always better than that of the SS EWMA-V chart, no matter whether the shift is small, medium, or large. The simulation studies for a normal process, exponential process, and the mixed process of normal and exponential distribution prove that our chart can quickly detect a small, medium, or large shift of process variance. Moreover, a numerical example of service time is used to illustrate the application of the proposed Bayesian EWMA variance chart and confirm the performance of process control.  
However, when monitoring the mixed process, the in-control ARLs seem to be biased due to the occurrence of the bimodal random behavior of \(p\), which will limit the use of the proposed chart, especially in situations that have a large dispersion of skewness. A future study could consider multimodal prior distribution to catch the random behavior of \(p\) resulting from the skewness-time-varying process. Moreover, this study does not test or compare the effects of different weights under a mixed distribution. This may be a good issue for further studies.  
Conceptualization, M.-C.L., C.-H.L., S.-F.Y., and M.-Y.L.; methodology, M.-C.L., C.-H.L., S.-F.Y., and M.-Y.L.; validation, M.-C.L., C.-H.L., S.-F.Y., and M.-Y.L.; investigation, C.-H.L., M.-C.L., S.-F.Y., and M.-Y.L.; data curation, M.-C.L., C.-H.L., S.-F.Y., and M.-Y.L.; writing--original draft preparation, M.-C.L., C.-H.L., S.-F.Y., and M.-Y.L.; writing--review and editing, M.-C.L., C.-H.L., S.-F.Y., and M.-Y.L.; supervision, S.-F.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 3: The Bayesian EWMA variance chart under the out-of-control process.
# Eye Fatigue Detection through Machine Learning Based on Single Channel Electrooculography
## 6 Conclusions  
Based on a comprehensive measurement of 12 subjects, this article discusses the correlation between EOG signal and scenarios on individuals' eye fatigue. This study develops portable EOG measuring equipment and proposes a method for detecting eye fatigue based on a single-channel EOG signal only. Despite the current model needs to be improved for detection in the eye relax state, this article still objectively demonstrated that EOG signal could be used as a significant basis for detecting eye fatigue which introduces a possibility of a new self-testing model for daily eye health and has a broad application prospect in the field of eye health care.
###### Abstract
The final layer is acellular and contains approximately twice the amount of platelets found in peripheral blood. It is worth noting that L-PRF uses the patient's own blood, removing any concerns about disease transmission. This should encourage clinicians to incorporate L-PRF into their practice [9; 10; 11]. L-PRF is currently being used in a number of procedures, including the treatment of drug-induced osteeoncrosis of the jaw, extraction socket healing, and pre-prosthetic bone surgery [12; 13; 14; 15]. The benefits of L-PRF in impacted third molar surgery are numerous and range from reducing the amount of pain and swelling the patient experiences after surgery to increasing the rate of soft tissue and bone healing. L-PRF provides a scaffold for hard and soft tissue to grow into and around, which is particularly important in impacted third molar surgery where there is a large bony defect to heal. The slow release of growth factors over the first week of healing is thought to significantly increase the speed of the healing process as growth factors are chemicals in the body that promote faster, more effective tissue repair. As a result, conventional surgical extraction in such cases often leads to potential complications, including severe pain, swelling, and trismus. Fortunately, with the development of platelet concentration techniques, surgeons now have the opportunity to use L-PRF in their daily practice [16; 17; 18; 19; 20]. The use of L-PRF in impacted third molar surgery has shown improved wound healing, reduced incidence of alveolar osteeitis, and improved postoperative recovery [13]. However, wound closure after surgery is an ongoing debate. When reviewing the studies evaluating the closure technique for lower impacted wisdom tooth surgery without L-PRF, the older studies recommend primary closure, while the current studies recommend secondary closure in terms of complications [6; 21; 22; 23; 24]. There are no studies reporting which closure technique is more successful in preventing or reducing postoperative complications after impacted lower third molar surgery when L-PRF is used. We believe that demonstrating the potential benefits of L-PRF on postoperative complications and which type of closure is more effective will ensure that L-PRF is used with the correct technique for this purpose. The aim of this study was to compare the effect of using L-PRF in patients undergoing impacted lower third molar surgery with either primary or secondary closure techniques. Our hypothesis is that there is no difference in L-PRF application between primary and secondary closure techniques in impacted lower third molar surgery.
## 4 Proposed Protocol Procedure
### Authentication and Key-Exchange Phase
#### 4.2.2 Step 3  
The third and final step concludes the key-agreement and authentication phase within the proposed protocol; the node verifies the server's authenticity with the assistance of the private parameters. Following the verification of both the accuracy of server-side information and the server's identity, the node proceeds to select the generated session key as the encryption key for securing data transmission. In the beginning stages, the node evaluates the freshness of the received message and moves forward with the protocol if the message is confirmed as fresh. Following this step, the node proceeds to compute the session key by utilizing both the received parameter (SA) and the parameters generated earlier in step 1 (A, S2, S3, ACi). As described in the node and server private key parameters protocol process, the ACi certificate plays a vital role in generating the effective session-key parameters. Therefore, the private parameters in session-key generation have significant effects on the implementation of security policies. In the next step, the node must ensure the identity of the server and the correctness of the received data. To accomplish this, the node recalculates the session key and S4 parameter, leveraging the parameters obtained in the previous steps, along with those sent by the server to the node. Then, the node compares the recalculated SK and S4' with the SK and S4 sent by the server. The parameters are evaluated according to the following procedure. 1 SK=H(S1 | A | S2 | S3 | SA | ACi)
2 S4=H(SK' | SA | N(y))  
If the values computed by the node match those received from the server, it indicates that the sender is the server and the received data is accurate. Subsequently, the session key produced is designated as the encryption key for maintaining secure communication. If any of the parameters used in the creation of the session key change under any circumstances, it will cause the value of the session key recalculated by the node to be different from the value received from the server. The changes directly affect the calculation result of the S4 parameter, and this parameter is used to authenticate the server and also check the accuracy of the information received from the server. Therefore, if the correctness of the values received by the node is not verified, the node will disconnect and prevent the continuation of the protocol process. Authentication of communication parties is one of the basic parameters in the presented protocol. We have used Ni, A, S2, S3, SK, and S4 parameters in the presented protocol process to implement the multiple authentication mechanism. Server and node entities at each step of the protocol recalculate certain parameters using the values they have, which helps them to evaluate the identity of the information sender.
# Development and Performance Evaluation of an Integrated Disc Cutter System for TBMs
## 3 Performance Evaluation of the ICS
### Selection and Analysis of the Evaluation Parameters of the ICS
#### 3.1.4 Static Strength  
The ICS should have enough strength to ensure its reliability when the cutters are under a large impact load. Taking solution 1 as an example, the finite element method is used to analyze the stress and deformation of the new ICS that applies nominal loads. The cutter ring will be damaged if the cutter receives a load greater than the nominal load, which is generally used to check the ICS in the project. The normal force, \(F_{N}\), lateral force, \(F_{S}\), and rolling force, \(F_{T}\), of the cutter under the nominal load [(20)] are 315 kN, 47.25 kN, and 31.5 kN, respectively, and the directions of the forces are shown in Figure 6a.  
Figure 4: Schematic diagram of removing cutters.  
Figure 5: Schematic diagram of the TBM cutter change space and drawing direction.  
In this paper, the stress state of the new tool system under a working load is analyzed by the linear elastic finite element method. The material of the TBM ICSs is Q345. Q345 is a low carbon alloy steel with yield strength of 345 MPa. The elastic modulus of Q345 is 206 GPa, and the Poisson's ratio of Q345 is 0.5. The corresponding international standard ISO 630-3:2012 (Structural steels--Part 3: Technical delivery conditions for fine-grain structural steels) is S355. The mesh model is mixed with tetrahedron and hexahedron elements, and the software used is ANSYS 18.0. The load input is shown in Figure 6a. The finite element analysis results of the ICS are displayed in Figure 6b,c. The analysis results show that the maximum stress and the maximum deformation of the ICS are 96.556 MPa and 0.7399 mm, respectively, under the nominal load. This shows that the safety factor of the ICS under nominal load is 3.58, which is higher than the required safety factor of 2.
# Evolution of Hybrid LiFi-WiFi Networks: A Survey
## 3 Performance of HWLNets
### Spectral Efficiency  
Spectral efficiency is an important consideration in designing and implementing visible light communication (VLC) systems, particularly in hybrid LiFi-WiFi networks that use dual-mode devices to transmit data over radio frequency (RF) and optical channels. In such networks, maximizing spectral efficiency is crucial to ensure that the available bandwidth is used as efficiently as possible. One approach to achieving high spectral efficiency in hybrid LiFi-WiFi networks is using differential carrier-offset electronic frequency division multiplexing (DCO-EFDM) modulation. This technique enables the simultaneous transmission of multiple data streams over a single optical carrier frequency, significantly increasing data throughput.  
DCO-EFDM modulation uses multiple subcarriers to modulate the amplitude and phase of the optical carrier signal. The subcarriers are closely spaced in frequency, with a small offset between each subcarrier. This allows for a higher spectral efficiency, as multiple data streams can be transmitted over the same optical carrier frequency without interfering with each other.  
Spectral efficiency can be interpreted as the quantity of data transmitted over a given spectrum or bandwidth with minimal transmission errors. The VLC system [29] was constructed with 4.85 bit/s/Hz spectral efficiency based on carrier less amplitude and phase modulation (CAP). Another paper denotes the use of generalized spatial modulation with dimming control to achieve results over 10 bit/s/Hz. Improvement in spectral efficiency was made with DCO-EFDM in [30] with adaptive bit load and a speed of 15.73 Gbps. HWLNets allow users to be moved from one to another network depending on SINR values, which in terms will increase spectral efficiency by up to 30% [5].  
Area spectral efficiency is defined to compare LiFi and WiFi systems by measuring the sums of max. average data rates of bandwidth on an area. Researchers [31] have determined that LiFi has 10-times more ASE than WiFi and hybrid networks will have at least 2-times more ASE than standalone networks. It has been found that small obstacles can actually prove beneficial to VLC because they block more interference than the desired wavelength [32].  
In summary, spectral efficiency is a critical consideration in designing hybrid LiFi-WiFi networks, and DCO-EFDM modulation is an effective technique for achieving high spectral efficiency in VLC systems. By leveraging the strengths of both VLC and WiFi technologies, hybrid networks can provide high-speed, reliable data transmission in a wide range of environments.
## 3 Sound Source Localization Fusion Algorithm of a Three-Plane Five-Element Microphone Array
### Relationship between Ranging and Direction-Finding and Fusion Algorithm  
According to the theory of indirect measurement error [30; 31; 32; 33], using the spherical coordinate calculation formula of the sound source S based on the X0Y, X0Z, and Y0Z planes, the measurement errors of \(r_{i}\), \(\theta_{i}\), and \(\varphi_{i}\) caused by the time-delay error \(\sigma_{\tau}\) are:  
\[\sigma_{r_{i}}=\frac{r_{i}c(\sqrt{a^{2}+r_{i}^{2}})}{a^{2}(4-cos^{2}\theta_{i })}\sigma_{\tau},i=0,1,2 \tag{9}\]  
\[\left\{\begin{array}{l}\sigma_{\theta_{0}}=\frac{\sqrt{2}c}{2 asin\theta_{0 }}\sigma_{\tau}\\ \sigma_{\theta_{i}}=\frac{\sqrt{2}c}{2 cos\theta_{i}^{2}}\sigma_{\tau},i=1,2 \end{array}\right. \tag{10}\]  
\[\left\{\begin{array}{l}\sigma_{\varphi_{0}}=\frac{\sqrt{2}c}{2 acus\theta_{0 }}\sigma_{\tau}\\ \sigma_{\varphi_{1}}=\frac{\sqrt{2}c\sqrt{1+tan^{2}\theta_{1}cos^{2}\varphi_{ 1}cos\theta_{1}}}{2 asin\varphi_{1}cos\theta_{1}}\\ \sigma_{\varphi_{2}}=\frac{\sqrt{2}c\sqrt{1+tan^{2}\theta_{2}sin^{2}\varphi_{ 2}}}{2 acus\theta_{2}}\sigma_{\tau}\end{array}\right. \tag{11}\]  
Substituting Equations (9) to (11) into Equation (8), the estimated errors of \(r\), \(\theta\), and \(\varphi\) are:  
\[\left\{\begin{array}{l}\sigma_{r}=k_{3}\sigma_{\tau}(k_{2}\sigma_{\tau_{0}}+ k_{1}\sigma_{r_{1}})+k_{4}\sigma_{\tau}\sigma_{\tau_{2}}\\ \sigma_{\theta}=k_{3}\sigma_{\tau}(k_{2}\sigma_{\theta_{0}}+k_{1}\sigma_{\theta _{1}})+k_{4}\sigma_{\tau}\sigma_{\theta_{2}}\\ \sigma_{\varphi}=k_{3}\sigma_{\tau}(k_{2}\sigma_{\varphi_{0}}+k_{1}\sigma_{ \varphi_{1}})+k_{4}\sigma_{\tau}\sigma_{\varphi_{2}}\end{array}\right. \tag{12}\]  
According to Equation (12), the distance estimation error \(\sigma_{\tau}\) is related to sound velocity \(c\), array element spacing \(a\), time-delay estimation error \(\sigma_{\tau}\), elevation angle \(\theta\), and itself, and is independent of horizontal angle \(\varphi\). Since the elevation angle \(\theta\) itself limits the value range, it has little influence on the ranging precision. The elevation angle estimation error \(\sigma_{\theta}\) is affected by the time-delay estimation error \(\sigma_{\tau}\), array element spacing \(a\), sound velocity \(c\), and itself, but not by the horizontal angle \(\varphi\). The horizontal angle estimation error \(\sigma_{\varphi}\) is related to the time-delay estimation error \(\sigma_{\tau}\), array element spacing \(a\), sound velocity \(c\), elevation angle \(\theta\), and itself. The ranging and direction-finding precision can be improved by appropriately increasing the spacing of the array elements and reducing the time-delay estimation error \(\sigma_{\tau}\).
###### Abstract  
This paper presents a comparative analysis of fiber aspect ratios using scanning electron microscopy (SEM) and the mean field homogenization approach. The novelty of this work lies in an effective fiber length evaluation based on a comparative analysis of fiber aspect ratios using scanning electron microscopy (SEM) and the mean field homogenization approach. This makes it possible to use an electron microscope to image fiber samples corresponding to the sample size using microtomography. Molded samples and pellets of four polyamide-6 short-glass fiber-reinforced composites with mass fractions of 15%, 30%, and 50% were considered. The aspect ratio distribution measured by SEM for the investigated materials was 20.25 with a coefficient of variation of 5.1%. The fiber aspect ratio obtained based on mean field homogenization theory and the tensile curve approximation was underestimated at 13.698 with a coefficient of variation of 5.2%. The deviation between the micro- and macro-estimates can be represented as a mean effective aspect ratio of 68% with a coefficient of variation of 8.5%. The developed technology for preparing samples for SEM and automated image processing can be used to study other short-reinforced polymer composite materials. The obtained estimates can serve as a useful reference when calibrating other models of short-fiber-reinforced polymer materials.  
glass short fibers; aspect ratio; effective fiber length; image segmentation; tensile test; electron microscope
# A High Gain Modified Quadratic Boost DC-DC Converter with Voltage Stress Half of Output Voltage
## 4 Formulation of Power Loss of the Proposed Studied Topology
### Power Losses in the Inductor Due to ESRs  
The Root mean square current through the inductor is notified as \(i_{LX_{rms}}\). The ESRs and the associated power loss are denoted as \(r_{LX}\) and \(P_{LX}\).  
\[\left\{\begin{array}{c}i_{L1_{rms}}=\frac{2<i_{0}>}{(1-d)^{2}}\\ P_{L1}=i_{L1_{rms}}{}^{2}r_{L1}=\frac{4<i_{0}>^{2}}{(1-d)^{2}}r_{L1}=\frac{4< i_{0}>^{2}}{(1-d)^{3}}\frac{r_{L1}}{K}P_{0}\\ i_{L2_{rms}}=\frac{2<i_{0}>}{(1-d)}\\ P_{L2}=i_{L2_{rms}}{}^{2}r_{L2}=\frac{4<i_{0}>^{2}}{(1-d)^{2}}r_{L1}=\frac{4< i_{0}>^{2}}{(1-d)^{2}}\frac{r_{L1}}{K}P_{0}\\ \end{array}\right. \tag{54}\]  
The total loss because of the ESRs of the Inductor can be obtained as  
\[P_{Ltotal}=\sum_{i=1}^{2}P_{Li}\]
# Magneto-Acoustic Imaging in Biology
## 4 Maet  
### [PERSON]'s Measurement of Current Using the Ultrasonically Induced Lorentz Force  
[PERSON] was a graduate student at the National Institute for Health and Medical Research in Lyon, France, when he published--with [PERSON], [PERSON], and [PERSON]--the 2001 article \"Electric Current Generated by Ultrasonically Induced Lorentz Force in Biological Media\" [50]. Their sample was a volume of pig blood. They applied a brief pulse of ultrasound in the \(z\) direction; 5 cycles of a 500 kHz wave with an amplitude of 1.5 MPa. The sample was positioned in a nearly uniform, static, 0.35 T magnetic field oriented in the \(x\) direction, which was produced by a permanent magnet. The Lorentz force on the moving ions in the blood separated the negative and positive charge, resulting in an electrical current in the \(y\) direction detected by two electrodes that were attached to a current-to-voltage converter (Figure 6). A typical voltage response had an amplitude of about 10 mV.  
One of [PERSON]'s goals was to confirm that the signal arose from the Lorentz force. He repeated the experiment using an iron \"phantom\" that had the same shape and size as his permanent magnet but was not magnetized; the response disappeared. He varied the distance between the sample and the ultrasound transducer and observed a time shift in the electric signal that was consistent with the speed of sound. He turned off the ultrasound transducer and the current vanished. He varied the electrical conductivity of his sample and saw a corresponding change in the current. He concluded that \"the signal was really created within the medium by Lorentz force: no electrode effect, no signal in the absence of ultrasound or magnetic induction, signal linearly shifted against the distance of the measurement chamber from the transducer, signal proportional to the electric conductivity, as predicted by the theory.\" Although he did not use the term \"Magneto-Acousto-Electrical Tomography,\" his paper nevertheless established the physical basis of MAET.  
In a follow-up article, [PERSON] and his colleagues applied their method to imaging electrical conductivity [51]. They found that the technique was most sensitive to conductivity gradients, and in particular boundaries between regions of different conductivity. They carried out experiments with agar gel phantoms and everyone's favorite tissue for studying conductivity: bacon. They could estimate the center of their 10 us ultrasound pulse to slightly better than one microsecond. These pulses were propagated at a speed of about 1500 m/s, providing a spatial resolution of approximately 1 mm. They contended that \"the advantages of this new modality for tissue characterization include the permeability of body tissue to magnetic field and ultrasound, the harmlessness of the applied fields and the improved spatial resolution in the measurement of a tissue's electric conductivity distribution.\"
# Evaluation of Green and Grey Flood Mitigation Measures in Rural Watersheds
## 1 Introduction
Quantitative evaluation of different flood mitigation measures is necessary to select the most effective strategy for flood risk management or for adaptation to climate change [16]. The flood risk assessment, which is generally aimed at evaluating the potential consequences of floods [17], provides a basis for evaluating different mitigation options and making optimal decisions on flood mitigation measures. The flood risk assessment can also serve other various purposes such as identifying vulnerability of communities to floods, developing flood risk maps, or financial appraisals for the insurance sector [18]. The goal of this research is to identify a method of evaluating general flood mitigation strategy for smaller, predominately rural watersheds. The paper presents an evaluation of different flood mitigation scenarios (grey, green, and combined green and grey) for a small rural, agricultural watershed in Serbia through a comprehensive flood risk assessment of each option and their comparison. The evaluation is made with a spatial damage model based on combination of flood hazard and vulnerability assessments. The model can be characterized as the micro-scale flood damage model because it uses high-resolution hazard map outputs from the hydrodynamic modelling and fine-detail, multi-category assets data. The damage assessment is validated against the official post-disaster financial loss appraisals following a major flood event in the region in 2014, and therefore produces estimates of tangible direct and indirect losses with relatively low uncertainties. The flood mitigation options are compared in a traditional way by calculating benefits as avoided damages and losses.
# Efficient Transmit Antenna Subset Selection for Multiuser Space-Time Line Code Systems
## 4 Complexity Analysis  
For complexity analysis of TAS selection algorithms, we consider the number of real multiplications (RMs) and the number of real summations (RSs) [24; 27]. Given arbitrary matrices \(\mathbf{A}\in C^{N\times M}\) and \(\mathbf{B}\in C^{M\times P}\), the number of complex multiplications (CMs) and complex summations (CSs) required for three matrix-related operations is given in Table 1[27], which is utilized in the following complexity analysis. Here, a CM requires four RMs and two RSs, whereas a CS uses two RSs.  
Recall that this work has employed an \(N_{T}-\)by\(-2K\) MU-STLC system with \(N_{T}\) available transmit antennas, \(N_{S}\) selected transmit antennas, and \(K\) users, where the receiver of each user has two receive antennas for STLC. Thus, it is assumed that the total number of receive antennas is \(2K\).