esa-sceva/satcom-chunk-collection huggingface dataset:
# Digital Photoelastic Analysis of TAD-Supported Maximly Arch Distalization
## 4 Discussion
### Clinical Implications  
The following clinical implications can be considered based on the stress distribution and direction analysis using digital photoelastic analysis in this study.  
1. TAD positioned to retract from the cervical region  
When a short hook is used, the crown is tipped distally during distal movement, and the molar will be intruded. However, with the middle hook, the vertical movement will be decreased. With the long hook, rotational movements in the anterior-superior direction were observed and applied molar downward direction, which increases the possibility of molar extrusion. Therefore, evaluating facial type in a clinical situation is crucial before selecting a hook length. This is due to the report [29] that, because the masticatory muscle activity is weak in high-angle facial types, resistance to molar extrusion is weak and mandibular rotation in the posterior-inferior direction and Class II relation could worsen as a consequence. Therefore, the short hook should be recommended for the high-angle case to distalize maxillary molars with the cervical position of TAD.
2. TAD positioned to retract from the apex  
In this situation, the molars tend to intrude while the dentition moves distally (Figure 13); therefore, it is presumed that this position is suitable for high-angle cases of Class II division 1. In addition, a long hook is considered advantageous to perform torque movement on lingually inclined maxillary anterior teeth, such as in Class II division 2.  
Figure 12: Supposed two centers of rotation; each CR functions in the opposite direction. CR1: the center of rotation of dentition, which is assumed to be positioned at the cervical half or third of the distal root surface of the second premolars; CR2: the center of rotation of the junction of the main arch and hook.  
In analyzing all combinations, the maxillary arch will be distalized parallel to the occlusal plane in the settings with the cervical position of TAD and medium hook, and the apical position of TAD and long hook.
## 3 Methods
### Optimization Algorithm  
To interpret the optimization results and prevent the problem of local optima due to a single algorithm, this paper utilizes both the particle swarm optimization (PSO) and crow search optimization (CSO) algorithms concurrently to optimize the objective function.  
The particle swarm optimization (PSO) is an optimization algorithm inspired by biom mimicry principles that emulates the collaborative behavior observed in bird flocks or fish schools to identify the optimum solution to a problem. This algorithm operates with a set of entities called particles. Each particle represents a potential solution in the solution space. These particles iteratively adjust their positions and velocities to find the best solution.  
The crow search optimization (CSO) is a heuristic optimization method based on bird behavior, simulating the crows' strategized foraging activities to ascertain the optimal solution. The foundational idea behind the CSO is to perceive the problem awaiting optimization as a search space, with the positions within this space representing potential solutions since crows optimize the quality of solutions through cooperative and competitive behaviors.  
In the optimization methodology adopted in this study, the PSO algorithm is implemented with a population of 40 particles undergoing a maximum of 100 iterations. The learning factors are fixed at 1.5 for both components, complemented with maximum and minimum inertial weights of 0.8 and 0.4, respectively. Meanwhile, the CSO algorithm has a population of 40 crows, a maximum iteration cycle of 100, a flight distance parameter of 2, and an exploration probability of 0.2.
# Front-End Amplifiers for Tuning Forks in Quartz Enhanced PhotoAcoustic Spectroscopy
## 5 Conclusions  
In this work, four different architectures were tested for the front-end amplifier electronics of a QTF employed as an optoacoustic transducer in a QEPAS sensor. The QEPAS spectra measured for H\({}_{2}\)O vapor in air show that the differential architecture improves the SNR, with respect to the single-ended configuration for both transimpedance and voltage amplifiers. This confirms that the differential structure allows for an increase in the SNR. Moreover, the performed experiments show that the voltage configuration of the QEPAS preamplifier based on an AD623 instrumental amplifier has better noise properties with respect to the transimpedance configuration employing with OP184. The AD623-based fully differential voltage amplifier results in a SNR\({}_{\text{FDV}}\) 1.4 times higher than SNR\({}_{\text{DT}}\) measured with a differential output transimpedance amplifier, and 1.8 times higher than SNR\({}_{\text{ST}}\) measured with the commonly employed single-ended transimpedance amplifier, both based on the OP184 component. All of these measurements pave the way for designing an application-specific integrated circuit (ASIC). The integrated circuits will allow for a better signal path matching and thus, further enhancing the QEPAS SNR.  
Conceptualization, All; methodology, All; measurements, P.Z.W., T.S., G.M., S.D.R., A.S., M.G.; preamplifiers design, P.Z.W.; investigation, G.M., A.S., P.P., M.G.; data curation, All; writing--original draft preparation, G.M., A.S., M.G.; writing--review and editing, All; supervision, V.S. All authors have read and agree to the published version of the manuscript. The authors from Dipartimento Interateneo di Fisica di Bari acknowledge the financial support from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie project OPTAPH, grant No. 860808 and from THORLABS GmbH within the joint-research laboratory. The authors from Warsaw University of Technology work acknowledge the financial support granted by the Polish Ministry of Science and Higher Education. [PERSON] acknowledges support by the Robert Welch Foundation (Grant No. C0586). The authors declare no conflict of interest.
# Optical Amplifiers for Access and Passive Optical Networks: A Tutorial
###### Abstract  
For many years, passive optical networks (PONs) have received a considerable amount of attention regarding their potential for providing broadband connectivity, especially in remote areas, to enable better life conditions for all citizens. However, it is essential to augment PONs with new features to provide high-quality connectivity without any transmission errors. For these reasons, PONs should exploit technologies for multigigabit transmission speeds and distances of tens of kilometers, which are costly features previously reserved for long-haul backbone networks only. An outline of possible optical amplification methods (2R) and electro/optical methods (3R) is provided with respect to specific conditions of deployment of PONs. We suggest that PONs can withstand such new requirements and utilize new backbone optical technologies without major flaws, such as the associated high cost of optical amplifiers. This article provides a detailed principle explanation of 3R methods (reamplification, reshaping, and retiming) to reach the extension of passive optical networks. The second part of the article focuses on optical amplifiers, their advantages and disadvantages, deployment, and principles. We suggest that PONs can satisfy such new requirements and utilize new backbone optical technologies without major flaws, such as the associated high cost.  
reamplification; reshaping; retiming; optical amplifiers; raman amplifiers; EDFA; SOA +
Footnote †: journal: Applied Physics Letters
## 3 Differential GNSS  
A single GNSS (global navigation satellite system) receiver can achieve an accuracy in the meter range. If a higher accuracy is required, a differential GNSS (DGNSS) can be used. DGNSS are based either on code- or on phase-range measurements. The accuracy of code-range-based differential techniques will improve compared to that of single receiver accuracy, but this is usually not as accurate as the carrier-phase differential measurement. Generally, differential GNSS measurements determine the position of a rover station related to a base station whose position is accurately known.  
To determine the phase-range, the receiver mixes the incoming satellite signal with a locally-generated signal [43]. Then, the calculation of the phase-range \(L_{A}^{j}(T_{A})\) can be achieved by [43]:  
\[L_{A}^{j}(T_{A})=c(T_{A}-T^{j})+B_{A}^{j} \tag{1}\]  
with \(c\) = 299,792,458 m s\({}^{-1}\) in vacuum and time \(T_{A}\) being the time when the signal was received. Time \(T^{j}\) resembles the time when the signal was transmitted by the satellite. \(T_{A}\) includes additionally a time offset \(\tau\) originating from the clock offset of the receiver. By assuming \(t\) to be the true system time, \(T_{A}\) can be expressed as \(T_{A}=t+\tau_{A}\). Likewise, \(T^{j}\) includes \(\tau^{j}\), which represents the clock offset of the satellite from the system time, that is \(T^{j}=t+\tau^{j}\). The subscripts \(A,B,C\), etc., indicate receiver-specific quantities, and the superscripts \(j,k,l\), etc., indicate satellite-specific quantities and with \(B_{A}^{j}=\lambda_{0}(\varphi_{0A}-\varphi_{0}^{j}-N_{A}^{j})\)[43]. Here, \(\lambda_{0}\) is the wavelength of the carrier signal; \(\varphi_{0A}\) is the initial phase of the receiver local oscillator; and \(\varphi_{0}^{j}\) is the initial phase of the transmitted satellite signal at initial time \(t_{0}\). \(N_{A}^{j}\) is called carrier-phase integer ambiguity [44]. \(B_{A}^{j}\) is not an integer, but includes unknown phase offsets of the receiver and satellite [43].  
The first term of Equation (1) is the pseudorange \(\rho_{A}^{j}\) from satellite \(j\) to receiver \(A\). Accordingly, including clock biases and atmospheric error terms, Equation (1) can be written as [43]:  
\[L_{A}^{j}(T_{A})=\rho_{A}^{j}+c(\tau_{A}-\tau^{j})+Z_{A}^{j}-I_{A}^{j}+B_{A}^{ j}+\epsilon_{p} \tag{2}\]  
with \(Z_{j}^{A}\) being the message delay due to the troposphere, \(I_{j}\) being the delay due to the ionosphere and \(\epsilon_{p}\), which incorporates measurement errors related to receiver hardware and multipath effects. Now, as the goal is to achieve the precise position information, Equation (2) includes still several uncertainties (clock biases, ionospheric delay and tropospheric delay) that need to be taken care of. The differencing techniques introduced in the following paragraphs can be used to mitigate these effects.
# Improvement of Global Ionospheric TEC Derivation with Multi-Source Data in Modip Latitude
## 2 Data and Analysis Method
### Ionospheric Observation Data
#### 2.1.2 Observation from JASON-2 Satellite Altimetry  
Observations from JASON-2 have a temporal resolution of 1s but only cover ocean areas. Since the ionospheric effects on electromagnetic wave paths are proportional to the density of free electrons and inversely proportional to the square of the frequency of the electromagnetic wave, the altimeter (Ku and C dual-band emission) equipped on JASON-2 can provide ionospheric range correction from the sea level up to the satellite orbit altitude. The measurement error caused by the instrument hardware, satellite attitude, waves or other factors is usually no more than 2\(\sim\)3 TECU (1 TECU = \(10^{16}e/\)m\({}^{2}\)), and the credibility of JASON-satellite-derived VTEC at low latitudes and equatorial regions is high [22].
# Data Processing and Analysis of Eight-Beam Wind Profile Coherent Wind Measurement Lidar
## 2 Materials and Methods
### Methods of Wind Field Retrieval  
The Doppler effect is used in velocity measurements of wind profile Lidar [35; 42]. When there is a relative velocity between the target and the Lidar, the received echo signal will produce a frequency shift relative to the transmitted signal, which is the Doppler frequency shift. By extracting the Doppler frequency shift of the echo signal, the wind speed information can be retrieved.  
The relationship between the Doppler frequency shift and the radial velocity is as follows:  
\[f_{d}=f_{r}-f_{t}=\frac{2V_{r}}{\lambda}, \tag{5}\]  
where \(f_{d}\) is the Doppler frequency shift, \(f_{r}\) is the frequency of the echo signal, \(f_{t}\) is the frequency of the output laser, \(V_{r}\) is the radial velocity of the target moving toward the radar, and \(\lambda\) is the laser wavelength. Therefore, the wind profile coherent wind measurement Lidar can effectively measure the space atmospheric wind field by checking the Doppler frequency shift of the echo signal. In this study, the frequency modulation \(V_{m}\) used in the wind profile coherent wind measurement Lidar was 80 MHz. When the wavelength was 1550 nm and the frequency modulation was 80 MHz, according to the Doppler frequency shift formula, the range of the measurable wind speed was \(\pm\)62 m/s.  
The atmospheric wind field is usually a function of space and time and has three-dimensional vector characteristics. Therefore, the measurement of the seasonal wind field at a certain position often requires the measurement of the three vector components of the wind field. However, a single-beam Lidar system can only measure the component of the wind vector along the line of sight of the laser beam. To accurately calculate the three-dimensional vector distribution of the wind field, at least three Lidar beams must pass the consistency test.  
In this study, the sine-fitting method was used to retrieve the radial velocities from the VAD scanning. The sine-fitting method uses the azimuthal angular distribution of the radial velocity to find a fitted sine-curve function in the form described by [PERSON] et al., [PERSON] et al. and [PERSON] et al. [45; 46; 47]. The following is an example of such an equation:  
\[v_{r}=a+bcos(\theta-\theta_{max}), \tag{6}\]  
Figure 4: Power spectrum of the raw data.  
The three-dimensional wind vector \(V\) is calculated using Equation (7) with the same constants as in Equation (6):  
\[V=(u,v,w)=\left(-\frac{bsint\theta_{max}}{cos\varphi},-\frac{bcos\theta_{max}}{ cos\varphi},-\frac{a}{sin\varphi}\right), \tag{7}\]  
Constants \(a\), \(b\), and \(\theta_{max}\) in Equation (6) are the offset, amplitude, and phase shift of the sine curve that best fits the VAD scan, respectively. \(\theta\) and \(\varphi\) are the azimuth and elevation angle of the lidar beam, respectively. The horizontal wind direction \(\gamma\) is expressed as follows:  
\[\gamma=\arctan\Bigl{(}\frac{u}{v}\Bigr{)}+\pi\{1-sign[(v+|v|)\cdot u]\},v \
eq 0, \tag{8}\]
# Mechanism to Estimate Effective Spectrum Availability Inside Smart Buildings
## 3 System Model
### Interference Conditions Model  
Based on this scenario, the two interference conditions necessary to estimate the ESA are explained; the first condition determines the use of the incumbent spectrum, and the second checks whether interferences are produced toward the deployment of primary band users inside the building.  
The first condition is called Condition 1 (C1), and its purpose is to establish the absence or presence of the PS. C1 allows the identification of the apartments where the interference \(I_{BS-SS}\) received in the SS from the BS would not be at a sufficient level to overcome a reference level \(P_{TH}\). It should be clarified that \(I_{BS-SS}\) was called interference because the SS was operating in its own band (e.g., the ISM band),  
Figure 2: Description of the scenario for the parameters, interference conditions, and ESA per floor.  
and any other received signals were considered interference. The reference power level \(P_{TH}\) used in this work was the sensitivity of a digital terrestrial television receiver.  
Since the determination of C1 involves both an external path from the BS to the SB with a LOS and an internal route inside the SB, several propagation models were evaluated for use. For the case of the interior path, the model of the penetration of buildings without a line of sight (NLOS) was evaluated, and for the LOS component, unlike in [23; 24], three known propagation models were evaluated: the Free Space Model (FRIIS) [25], the Egli Propagation Model (EGLI) [26], and the LOS component of the COST 231 Walfisch-Ikegami (COST-WI) Model [25]. The FRIIS model was considered because it is a good indicator of path losses, the EGLI model was used because it considers wave propagation on uneven terrain, and the LOS component of COST-WI was considered because is a widely used reference; thus, in this work, the propagation model with the best correlation factor concerning two measurement campaign results was chosen.  
The second condition, called Condition 2 (C2), allows the determination of the apartments from which an SR-CRWS would not cause interference to the PS receiver located on the rooftop of the building. For this condition to be met, the interference level \(I_{SS-PS}\) could not exceed the minimum threshold level supported by the PS receiver; the threshold level, as in C1, was the sensitivity of a TV receiver. Consequently, both C1 and C2 are described as follows [24]:  
\[C1:\ \left\{\begin{array}{ll}True,&if\ \ I_{BS-SS}<P_{TH}\\ False,&if\ I_{BS-SS}\geq P_{TH}\end{array}\right. \tag{1}\]  
\[C2:\ \left\{\begin{array}{ll}True,&if\ \ I_{SS-PS}<P_{TH}\\ False,&if\ I_{SS-PS}\geq P_{TH}\end{array}\right. \tag{2}\]  
To calculate C2, the Multi-Wall Model (MWM) [25] was used to evaluate the measurement above the campaign. The apartments where C1 and C2 were true made up the region with the ESA.
# Anomaly Detection in Automotive Industry Using Clustering Methods--A Case Study
## 2 Clustering Algorithms
### Differential Evolution (DE)  
Differential Evolution (DE) is another technique inspired by the evolution of species. In the literature, it has proved to be a viable candidate for optimization and clustering problems. In this case, agents are called vectors and are stochastically generated as in PSO and GA [61].  
The positions of the vectors provide valuable information about the cost function scenario. As long as a good uniform randomization method is used to build the initial population, the initial agents provide a good representation of the entire search space, with relatively large distances between them. As the search progresses, the distances between the vectors decrease, with everyone converging on the same solution. The magnitude of the initial distances between individuals is influenced by the population size in an inversely proportional way [57].  
In DE, a population of _NP_ candidate solutions indicated as \(\mathbf{x}_{i,G}\), where \(i\) denotes each agent and \(G\) represents the generation of the population, is used. As in GA, the operators are crossover, mutation, and selection [62].  
The optimization process starts with selecting a vector, named target vector, and three others were chosen at random: \(\mathbf{x}_{r1}\), \(\mathbf{x}_{r2}\), and \(\mathbf{x}_{\gamma 3}\). In the next step, the mutation operator is applied, generating a new donor vector \(\mathbf{v}_{i,G}\) through Equation (13):  
\[\mathbf{v}_{i,G}=\mathbf{x}_{r1,G}+F\times(\mathbf{x}_{\gamma 3,G}+\mathbf{x}_{r2,G}) \tag{13}\]  
Figure 4: Gene mutation of chromosomes with length \(l=8\).  
Figure 3: Single-point crossover for a pair of chromosomes of length \(l=8\).  
where \(F\) is a user-defined constant from \([0,2]\), and \(i=1,\ldots,\)_NP_, \(r_{1},r_{2},r_{3}\in\{1,\ldots,\)_NP_\(\}\).  
Then, a crossover operator, also called recombination, incorporates successful solutions into the population. The trial vector \(\mathbf{u}_{i,G}\) is created by the binomial combination of the target vector \(\mathbf{x}_{i,G}\) and the donor vector \(\mathbf{v}_{i,G}\) elements. Each element of the _trial vector_ comes from \(\mathbf{x}_{i,G}\) with crossover probability \(C_{r}\in[0,1]\) selected along with the population, as in Equation (14) [62]:  
\[\mathbf{u}_{j,\;i,G+1}=\left\{\begin{array}{l}\mathbf{v}_{j,i,G+1}\text{ if }\text{rand}_{ij}[0,\;1]\leq C_{r}\text{ or if }\text{j}=I_{rand}\\ \mathbf{x}_{j,i,G+1}\text{ if }\text{rand}_{ij}[0,\;1]>C_{r}\text{ or if }\text{j}\
eq I_{rand}\end{array}\right. \tag{14}\]  
Finally, the selection operator differs from GA. In this case, the target vector \(\mathbf{x}_{i,G}\) is compared with the trial vector \(\mathbf{u}_{i,G}\), and the corresponding vector with the best fitness value is taken into next-generation, agreed approach, as in Equation (15):  
\[\mathbf{x}_{i,G+1}=\left\{\begin{array}{l}\mathbf{u}_{i,G+1}\text{ if }f(\mathbf{u}_{i,G+1})\leq f(\mathbf{x}_{i,G})\text{ where }i=1,2,\ldots N\\ \mathbf{x}_{i,G}\text{ otherwise}\end{array}\right. \tag{15}\]
###### Abstract  
Third-harmonic generation (THG) is of interest for its various applications. We propose using the cascaded second-harmonic generation (SHG) to implement the frequency conversion process, which is similar to that occurring in a medium with cubic susceptibility. Physically, the process is based on consecutive generation of the second-harmonic and the sum frequency in the same crystal with quadratic susceptibility at large phase mismatching between the fundamental wave and the second-harmonic wave. In this case, at phase matching between the fundamental wave and the third-harmonic wave, THG occurs with high efficiency. To demonstrate such a possibility theoretically, we apply the multi-scale method to a set of Schrodinger equations, describing a three-wave interaction with the frequencies \(\omega\), \(2\omega\) and \(3\omega\) in a medium with quadratic susceptibility, to derive modified equations describing the frequency tripling process. These equations are solved without using the fundamental wave energy non-depletion approximation. A THG efficiency equal to 94.5% is predicted theoretically. The analytical solution is confirmed by computer simulation results. We study how various factors, such as the incident pulse intensity, phase mismatching between interacting waves, group velocity mismatching of the pulses, and second-order dispersion of the wave packets influence the THG process.  
nonlinear optics; harmonic generation; ultraviolet generation; frequency tripling