esa-sceva/satcom-chunk-collection huggingface dataset:
# Error Probability of a Coherent \(M\)-ary PSK FSO System Influenced by Phase Noise
## 3 Error Rate Analysis
### Convergence Analysis  
The SEP expression in (22) is re-written as  
\[P_{S}=1-\frac{1}{M}-2\pi B, \tag{23}\]where \(B=\sum\limits_{n=1}^{\infty}D_{n}\sin(n\pi/M)\) and \(D_{n}=b_{n}c_{n}/n\). It holds \(b_{n}>b_{n+1}>0\) and \(c_{n}>c_{n+1}>0\), thus  
\[D_{n}=\frac{b_{n}c_{n}}{n}>\frac{b_{n+1}c_{n+1}}{n+1}=D_{n+1}. \tag{24}\]  
In addition, it is true that  
\[\lim\limits_{n\to\infty}D_{n}=\lim\limits_{n\to\infty}\frac{b_{n}c_{n}}{n}=0. \tag{25}\]  
Furthermore, according to ([33], (1.342.1)), it holds that  
\[\sum\limits_{k=1}^{n}\sin\biggl{(}\frac{k\pi}{M}\biggr{)}=\frac{\sin\bigl{(} \frac{n\pi}{2M}\bigr{)}\sin\Bigl{(}\frac{(n+1)\pi}{2M}\Bigr{)}}{\sin\bigl{(} \frac{n}{2M}\bigr{)}}\leq\frac{1}{\sin\bigl{(}\frac{\pi}{2M}\bigr{)}}=\text{ const}. \tag{26}\]  
Based on (23), (24) and (25), by applying Dirichlet criterion ([33], (0.228.2) and (0.229)), it is proved that a series \(B\) in (23) is convergent.  
In order to estimate the truncation error in evaluating SEP, (22), i.e., (23), is presented in the form  
\[P_{\text{S}}=1-\frac{1}{M}-2\pi\sum\limits_{n=1}^{\infty}D_{n}\sin\frac{n\pi} {M}, \tag{27}\]  
where \(D_{n}\) is a decreasing zero-sequence defined before. Replacing \(n\) by \(M(n-1)+\
u\), \(\
u=1,\ldots,M\), and using the fact that  
\[\sin\frac{\pi}{M}[(M(n-1)+\
u]=(-1)^{n-1}\sin\frac{\
u\pi}{M},\quad\
u=1,\ldots,M, \tag{28}\]  
the previous formula for \(P_{\text{S}}\) becomes  
\[P_{\text{S}}=1-\frac{1}{M}+2\pi\sum\limits_{n=1}^{\infty}\sum\limits_{\
u=1}^ {M-1}D_{M(n-1)+\
u}(-1)^{n}\sin\frac{\
u\pi}{M}, \tag{29}\]  
because the last term in the inner sum vanishes (for \(\
u=M\)). We point out here that we do not change the order of the series terms, but only group them so that they are first negative, then positive, etc.  
Thus, \(P_{\text{S}}\) reduces to  
\[P_{\text{S}}=1-\frac{1}{M}+\sum\limits_{n=1}^{\infty}(-1)^{n}q_{n}, \tag{30}\]  
where  
\[q_{n}=2\pi\sum\limits_{\
u=1}^{M-1}D_{M(n-1)+\
u}\sin\frac{\
u\pi}{M}>0 \tag{31}\]  
and it is easy to see that \(\lim\limits_{n\to\infty}q_{n}=0\).  
With this procedure, we obtain an alternative series for which we have a remainder estimate if the series is approximated by a partial sum  
\[P_{\text{S}}=1-\frac{1}{M}+\sum\limits_{n=1}^{N}(-1)^{n}q_{n}+E_{N}, \tag{32}\]  
where \(|E_{N}|\leq q_{N+1}\).  
The convergence acceleration can be achieved by applying the Euler-Abel transformation ([36], SS1.3.2) as it was used in the recent article [37] (see also ([36], SS1.3.2)).
Optical Frequency References at 1542 nm: Precision Spectroscopy of the R(106)50-0, R(100)49-0, R(84)47-0, R(59)45-0, P(82)47-0, and P(71)46-0 Lines of \({}^{127}\)I\({}_{2}\) at 514 nm  
[PERSON]  
P(82)47-0, and P(71)46-0 lines of \({}^{127}\)I\({}_{2}\) at 514 nm. These lines have not been investigated using Doppler-free laser spectroscopy, and their hyperfine structures have not yet been resolved. The absolute frequencies of these lines were only measured for the Doppler-limited line (an overlap of all the Doppler-broadened hyperfine structures in the line) center using Fourier transform spectroscopy with an uncertainty of several tens of megahertz [11].  
In this study, we performed Doppler-free laser spectroscopy based on modulation transfer spectroscopy [12; 13] for the R(106)50-0, R(100)49-0, R(84)47-0, R(59)45-0, P(82)47-0, and P(71)46-0 lines of \({}^{127}\)I\({}_{2}\) at 514 nm. This study is a continuation of our previous works [8; 10] and focuses on some relatively weak iodine lines. The absolute frequencies and hyperfine transitions of the lines were measured with an uncertainty of 5.6 kHz, which is an improvement of approximately four orders of magnitude compared to that of the Doppler-limited measurement [11]. The hyperfine constants for each line were obtained by fitting the observed hyperfine structure to a four-term Hamiltonian equation [14]. The observed 97 hyperfine transitions of the six iodine lines provide new optical frequency references for telecommunications and other applications, including optical frequency combs. In telecommunication applications, the obtained new optical frequency references can be used for DWDM channels where no frequency reference is available. In the frequency comb system, the obtained frequency-stabilized laser in the telecom wavelength region can serve as the frequency reference for Er-fiber combs [7].
# Application of Genetic Algorithms for Periodicity Recognition and Finite Sequences Sorting
## 2 Related Work and the Current Contribution
### Applications  
The paper's contribution lies in proposing a new method for efficiently transforming nonperiodic sequences into their periodic analogs using a genetic algorithm, which can have wide applications in various fields such as data approximation and algorithm optimization. More specifically,  
1. **Chaos and Dynamic Systems Research:** Understanding and analyzing chaotic systems using periodicity processing methods.
2. **Bioinformatics:** Analyzing genetic sequences where periodicity may be related to specific genetic patterns or structures. Investigating periodicity in biological data, such as amino acid sequences, to identify patterns.
3. **Cryptography and Information Security:** Generating secure encryption keys, random initialization vectors, and secure random numbers for cryptographic applications.
4. **Signal Processing and Telecommunications:** Analyzing periodicity in signals to improve filtering, modulation, and decoding processes in telecommunication systems.
5. **Medical Diagnostics:** Researching periodicity in biomedical data to detect regular patterns associated with diseases or physiological processes.
6. **Financial Analytics:** Analyzing time series of financial data to identify periodic trends and patterns in the market.
# Driverless Bus Path Tracking Based on Fuzzy Pure Pursuit Control with a Front Axle Reference
## 3 Simulations
### Simulation of Longitudinal Control  
In this section, an experiment is conducted to verify the efficiency of the longitudinal controller. Since the acceleration is controlled by the VCU on the bus, only deceleration is discussed in this part.  
The initial velocity of driverless bus is 40 km/h. The cycle of the controller is 100 ms. The slope of the road is 0. Table 5 shows the parameters of the longitudinal control. Figure 18a shows the simulation scene. Figure 18b shows the designed velocity and simulation results. Meanwhile, Figure 18c indicates the velocity error. Deceleration is shown in Figure 18d. The results illustrate that the system quickly converges to a steady state while there is a deviation and the steady-state deviation is almost 0. The simulation results illustrate the high tracking accuracy of the longitudinal controller. Additionally, deceleration changes continuously and does not exceed \(-0.6\) m/s\({}^{2}\). Meanwhile, the velocity of the driverless bus changes constantly, too. Due to the delay of the actuator and the inertia of the driverless bus, there is some slight overshoot in the actual velocity.  
Figure 17: The performances of PPC-RAR and PPC-RAR. (**a**) Desired path and bus’s path under different conditions. (**b**) Steering angle. (**c**) Lateral error. (**d**) Heading error.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{3}{c}{**Feedback Control**} & \multicolumn{3}{c}{**Feedforward Control**} \\ \cline{2-6}  & \(r\) & \(h\) & \(k_{p}\) & \(k_{i}\) & \(k_{v}\) \\ \hline value & 4 & 0.01 & 0.19 & 0.001 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters of longitudinal control for simulation.
## 3 Results
### Verification of FY3C/GNOS IRO Products in Ionospheric Climatology with COSMIC
#### 3.2.2 Ionospheric Climatological Characteristics of hmF2 between FY3C/GNOS and COSMIC
(**e,f**) The absolute errors of season median hmF2 between FY3C/GNOS and COSMIC at 0800–1100 LT and 2000–2300 LT, respectively. Obtaining of the hmF2 absolute error can be seen in Section 2.2. Figure 11: Ionospheric climatological characteristics of season median hmF2 observed by FY3C/GNOS and COSMIC and their differences at 0800–1100 LT and 2000–2300 LT during JS-month (2016.128–2016.218). The red arrow points to the diurnal variation of hmF2, in which the nighttime hmF2 enhancement, namely, the general WSA can be clearly observed. Refer to Figure 10 for more details. Figure 12: Ionospheric climatological characteristics of season median hmF2 observed by FY3C/GNOS and COSMIC and their differences at 0800–1100 LT and 2000–2300 LT during SE-month (2016.221–2016.311). Refer to Figure 10 for more detailed annotations.
## 4 Exploitation Risk Assessment for Vulnerability
### Collecting Vulnerability/Exploit Data  
The data utilized in this study can be broadly categorized into (1) vulnerability-related data and (2) exploit-related data. Vulnerability-related data consist of data from various sources, including common vulnerabilities and exposures (CVEs), common platform enumeration (CPE), common weakness enumeration (CWE), and common attack pattern enumeration and classification (CAPEC) datasets obtained from the National Vulnerability Database (NVD). In addition, we use insights from the MITRE ATT&CK framework, which provides an analysis of the tactics, techniques, and procedures used by various attack groups. To ensure a standardized source of information, we also used the Cybersecurity and Infrastructure Security Agency's (CISA) ICS-CERT Advisories. This source provides insight into the types of vulnerabilities in OT and ICS environments, thereby aiding the development of our assessment approaches.  
To classify exploit code maturity and assess severity within OT and ICS environments, we utilized exploit data from exploit DB and GitHub, which provide proof-of-concept (PoC) information as seen in previous studies, along with data from Exploitalert. We also referred to the Trickiest CVE Repository, which provides updated PoC information on publicly available vulnerabilities. To obtain information on exploited vulnerabilities, we leveraged known exploited vulnerabilities (KEV) data, which provide details on exploited vulnerabilities. We consulted the National Cyber Awareness System (NCAS), which provides information on security issues, vulnerabilities, and exploits in infrastructure and advanced persistent threat groups. In addition, we referred to the Zero-day Initiative, Vulners, and in the wild.io, which provided vulnerability details and exploits utilized in zero-day and in-the-wild attacks. We also used Rapid7's Metasploit, which provides automatically combined attack module information for vulnerabilities, to collect comprehensive exploit information for publicly available vulnerabilities.  
Each piece of collected vulnerability information is linked on the basis of specific attributes, while exploit data are linked to vulnerability information using the source vulnerability identifier, CVE. Figure 3 provides a visual representation of these connections.  
Figure 3: Connections between collected threat intelligence information.
## 4 Theoretical Calculation and Analysis  
### Thermodynamic Principle  
The concrete structure was assumed continuous and homogeneous. Considering an infinitesimal volume within the concrete structure, the net heat (calorie) inflows in the volume along the \(x,y\) and \(z\) directions are given as:  
\[(q_{x}-q_{x+dx}dydz)=\lambda\frac{\partial^{2}T}{\partial x^{2}}dxdydz \tag{1}\]  
\[(q_{y}-q_{y+dy}dxdz)=\lambda\frac{\partial^{2}T}{\partial y^{2}}dxdydz \tag{2}\]  
\[(q_{z}-q_{z+dz}dxdy)=\lambda\frac{\partial^{2}T}{\partial z^{2}}dxdydz \tag{3}\]  
Suppose that the heat produced per unit volume per unit time is \(Q\). The heat emitted by an infinitesimal volume during a unit time is \(Qdxdydz\). During the time \(d\tau\), the heat absorbed by the infinitesimal volume is \(c\rho\frac{\partial T}{\partial\tau}d\tau\)\(dxdydz\).  
Based on the thermal balance principle, the sum of the heat produced by the interior and the heat inflow is equal to the total absorbed heat as:  
\[\left[\lambda\left(\frac{\partial^{2}T}{\partial x^{2}}+\frac{\partial^{2}T} {\partial y^{2}}+\frac{\partial^{2}T}{\partial z^{2}}\right)+Q\right]dxdydzd \tau=c\rho\frac{\partial T}{\partial\tau}d\tau dxdydz \tag{4}\]  
Simplifying Equation (4),  
\[\frac{\lambda}{c\rho}\left(\frac{\partial^{2}T}{\partial x^{2}}+\frac{ \partial^{2}T}{\partial y^{2}}+\frac{\partial^{2}T}{\partial z^{2}}\right)+ \frac{Q}{c\rho}=\frac{\partial T}{\partial\tau} \tag{5}\]  
Due to the heat of hydration, the rate of temperature rise of concrete under adiabatic temperature condition is given as,  
\[\frac{\partial\theta}{\partial\tau}=\frac{Q}{c\rho}=\frac{Wq}{c\rho} \tag{6}\]  
Hence, Equation (5) could be revised as,  
\[\frac{\lambda}{c\rho}\left(\frac{\partial^{2}T}{\partial x^{2}}+\frac{ \partial^{2}T}{\partial y^{2}}+\frac{\partial^{2}T}{\partial z^{2}}\right)+ \frac{\partial\theta}{\partial\tau}=\frac{\partial T}{\partial\tau} \tag{7}\]  
Figure 9: D-value between the concrete surface and the surrounding environment.  
In Equations (6) and (7), \(\theta\) is the adiabatic temperature rise of concrete, \(W\) is the cement content of concrete (kg/m\({}^{3}\)), \(q\) is the heat source intensity (heat released by a unit volume of cement at a unit time), \(c\) is the specific heat of concrete, \(\rho\) is the density of concrete, \(\lambda\) is the thermal conductivity and \(T\) is the transient temperature of concrete.
## 4 Optimization Results  
The transient time-stepping finite element method was used to calculate the optimized electromagnetic performances. Simulation analysis was done for the combined parameters to compare the motor flux density, no-load back EMF, and output torque before and after optimization.  
Figure 6 gives the air-gap flux density of the AFPMBLDC. It can be seen that the air-gap flux density of the motor before optimization is 1.1 T, and the air-gap magnetic density is too large, which means that the air-gap of the motor is too small and the assembly requirements of the motor are high. After optimization, the air-gap flux density of the motor is 0.75 T, which is 31.8% lower, and the air-gap flux density waveform is closer to sinusoidal, which is conducive to reducing the torque ripple and loss and increasing the output torque.  
Figure 7 shows the comparison of the no-load back EMF before and after optimization of the AFPMBLDC. The total harmonic distortion rate of the optimized no-load back EMF waveform is reduced by 7.5% when AFPMBLDC are running at the rated speed. Compared with the no-load back EMF before optimization, the optimized no-load back EMF has an RMS value of 8.8 V, which is 8.6% higher.  
Figure 6: Air-gap flux density.  
The output torque graph of the AFPMBLDC is shown in Figure 8. The motor's average output torque before optimization was 0.504 Nm, while after optimization, the average output torque was 0.491 Nm, a 2.5 percent reduction. The motor torque ripple after optimization is 4.3 percent lower than before optimization, with a smoother waveform and improved motor stability.  
Figure 7: Back EMF.
## 1 Introduction
### Related Works
#### 1.1.3 LRSD-Based Methods  
Meanwhile, leveraging the work of [PERSON] et al. [21] in matrix optimization, low-rank sparse decomposition (LRSD)-based methods IPI, IMNNLWEC, and ILN-SSR [22; 23; 24] formulate small target detection as an optimization problem. Although these low-rank and sparse-based methods can detect targets in some scenarios, they struggle to perform effectively when faced with complex backgrounds such as forests or urban areas, where the background cannot be characterized as low-rank.
## 6 Conclusions  
Within the context of automated sensing, this investigation integrates the novel integration of an ultrasonic detection technology, Epoch(tm) 650, within the end effector of a KUKA KR 16 robotic system specifically for the application of the enhanced detection of railway track flaws. To provide an informed assessment of the developed system, a comparison was performed via a manual system. The main conclusions derived from the study are the following:  
* Both the manual and robotic configurations were proven to be ineffective at detecting surface crack defects when employing the 60\({}^{\circ}\) and 70\({}^{\circ}\) wedged probe.
* Both configurations were effective at detecting internal rail defects, perpendicular to the direction of the propagated incident ultrasonic wave, located in the web region of the rail.
* The characterization of internal defects by the robotic system with a speed of 6 mm/s was comparable to the actual defect size and to the values attained by the manual approach.
* Depth and location measurements of the internal flaws were shown to be comparable for both systems.  
This research was focused on the hypothesis that automation is viable for detecting flaws on rail tracks. Future research should now focus on the design of end effectors that can accommodate multi-transducers for increased ultrasonic testing. In addition, an end effector design that can accommodate a phased-array (PA) transducer should be studied. Compared with single-element systems, the PA uses a total focusing method (TFM) that provides full matrix capture (FMC). This would make the mapping of tracks much easier and more accurate than with traditional ultrasonic equipment.  
**Author Contributions:** Writing--original draft, L.C.; Writing--review & editing, A.R. and J.T.; Supervision, C.A.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** The authors would like to acknowledge the support of the Department of Mechanical Engineering, Future Manufacturing Research Institute, Faculty of Science and Engineering, Swansea University, and the Advanced Sustainable Manufacturing Technologies (ASTUTE 2022) project, in enabling the research upon which this paper is based. Further information on ASTUTE can be found at www.astitewales.com and [[https://www.swansea.ac.uk/science-and-engineering/research/engineering/astute/](https://www.swansea.ac.uk/science-and-engineering/research/engineering/astute/)]([https://www.swansea.ac.uk/science-and-engineering/research/engineering/astute/](https://www.swansea.ac.uk/science-and-engineering/research/engineering/astute/)) (accessed on 29 January 2014).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Crescent Microstrip Antenna for LTE-U and 5G Systems
## 6 Simulations and Measurements Results  
The designed antenna structure consists of three components: a ground plane, patch, and substrate. The analysis of the electrical parameters of the radiating element and other elements of the preliminary antenna model showed that it is possible to improve the electrical parameters of the antenna, such as reducing VSWR, increasing the bandwidth, miniaturization of the antenna dimensions, or increasing the energy gain. For this purpose, the optimization process was carried out in the CST Microwave Studio software in terms of the above-mentioned parameters. For the calculated parameters of the patch, a preliminary simulation of the electrical parameters of the developed antenna model was performed and the process of optimization of the structure was carried out, assuming that the main assumptions for the antenna remained unchanged [41; 42].  
For the final model of the antenna designed in this way, a simulation process was carried out using the CST Microwave Studio software, thus obtaining the results of the electrical parameters, such as the reflection coefficient, voltage standing wave ratio, input impedance, energy gain, antenna efficiency, antenna current distribution, and radiation patterns. Additionally, selected electrical parameters were measured for the physical model of the antenna. The appearance of the proposed antenna during the measurements is shown in Figure 6, during the measurements of electrical parameters and the measurement of radiation characteristics, respectively.  
Figure 4: The optimized antenna model view—front and back side with dimensions.  
Figure 5: The optimized physical antenna model view—front and back side with dimensions.
# Combined Use of MRI, fMRIand Cognitive Data for Alzheimer's Disease: Preliminary Results
## 2 Methods and Procedure
### Support Vector Machine Classifier  
As discussed in the Introduction, to help the clinicians to investigate the AD status, the use of machine learning has been explored in the literature [27; 28]. Among the available supervised approaches, Support Vector Machine (SVM) is an efficient supervised learning method, which can organize large dimensional data classification [67]. This linear classifier is very efficient in working with two-class linearly-separable tasks, i.e., with data of the binary class [67]. The classification operates finding the hyperplane with the maximum possible margin between the two classes.  
The capability and usefulness of the SVM classification method to discriminate AD patients from healthy ones was demonstrated in other studies [27; 28; 68]. However, it must be stressed that  
Figure 3: Connectivity matrices for the (**a**) AD group, (**b**) HC group, and (**c**) the difference HC-AD.  
these works made use of structural brain features only, i.e., morphological information derived from MRI [68]. Even when a reduced number of feature is employed, this method should be regarded as a partial and limited approach to the development of a reliable diagnostic tool.  
In contrast with other studies, which used different imaging modalities, such as FDG-PET [27] or investigated CSF [28], the approach proposed in this paper exploits fMRI. In these studies [27; 28], which presented both anatomical and another type of functional images, the number of subjects was comparable to the one used therein. In fact, in other studies that used MRI, such as [26; 39; 41], subjects presented only morphological images. Another study used only fMRI acquisition, presenting 40 subjects, 20 ADs and 20 HCs [42]. All these considerations allowed us to say that our number of subjects was sufficient for this type of analysis. The approach proposed in our paper may be of interest for other researchers, which could access a larger dataset.  
To train the SVM classifier, we used the leave-one-out method, as in [27; 42]. In this case, the analysis was repeated for each subject: in every iteration, one sample was left out to test the classifier, and the remaining subjects were fed to the classifier for training [69]. The procedure was repeated until all subjects were used at least once as the test sample. In this study, the SVM classifier was implemented in MATLAB.  
Three different SVM classifiers were tested. Firstly, the unimodal approach was assessed. Therefore, two different SVM systems were trained, one using the MRI measurements and the other one employing the fMRI-derived feature. Then, the MRI, fMRI, and MMSE information was combined to train a multimodal SVM classifier. The MMSE indicator was strongly correlated with the pathology and thus could favor the investigation between HC and AD subjects [29; 31; 70].