esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Zoom Optical System from Visible to NIR-II for Vivo Fluorescence Imaging Device
## 3 Optical Design and Optimization
### Initial Structure Selection
As shown in the following figure (Figure 4), the initial layout zoom range is 10-60 mm; the F-number is four, the total length is 100 mm, the full field of view angle is 4.2-25\({}^{\circ}\), and the working spectrum is the visible band. The layout of this four-group zoom optical system is shown in Figure 1. From left to right, the front fixed group, the zoom group, the diaphragm, the compensation group, and the fixed group can be observed. After determining the initial layout of the optical system, the first step is to scale it. This initial layout has a focal length of 10 mm-60 mm and should be enlarged appropriately. Table 3 is the selected normalized initial structure of each lens parameter table. Figure 4: Light path diagram of initial layout [16]. Figure 3: Quantum efficiency of InGaAs detectors adapted to this paper [10]. In the design process, the aberration is always larger in the mid-focal section, and the reason is found: the too-strict limitation of the rear intercept distance. In this step, the rear fixed group is considered to be transformed into a movable component, and during the zoom movement, the rear three components all move according to a certain trajectory. In this form, each component moves according to the most favorable way, which can achieve the maximum zoom effect. The design of the lens barrel of this form is more complex than other forms of the zoom system, and the total length of the optical system becomes variable, which makes the zoom system have three groups of zoom curves, but with the improvement of processing technology, its complexity and processing difficulty also decreases [17]. According to the imaging principle of the positive group compensated zoom optical system, when the focal lengths distribution pattern is '+, -, +, -', respectively, it is more suitable to reduce the length and size of the system and ensure a longer back intercept [18], because it constitutes a classic anti-telephoto structure. Therefore, the focal power distribution pattern of '+, -, +, -' is adopted in this paper. \[ \begin{cases}d_{12}=f_{1}^{\prime}-l_{2}\\ d_{23}=-l_{3}+l_{2}^{\prime}\\ d_{34}=l_{3}^{\prime}-l_{4}\\ d_{4f}=l_{4}^{\prime}\end{cases}  \tag{6}\]  
\[ \begin{cases}d_{12s}<d_{12m}<d_{12l}\\ d_{23s}>d_{23m}>d_{23l}\\ d_{34s}<d_{34m}<d_{34l}\\ d_{4 fs}<d_{4 fm}<d_{4 fl}\end{cases}  \tag{7}\]  
Assuming that the total length of the final zoom optical system is 190 mm, the short focal length m\({}_{4s}\) of group 4 is obtained proportionally as 1.3, which can be used as a reference. The initial layout of the wide band zoom optical system is obtained after several calculations and optimization according to the above formula. The focal lengths of each
# Ultrafast Optical Signal Processing with Bragg Structures
## 3 Optical Signal Processing in Resonantly Absorbing Bragg Reflector (RABR)
### Theoretical Considerations
The result shows that stable moving solitons can be generated, but the zero-velocity soliton no longer exists if the thickness of the two-level atom layer exceeds a critical value (1.2 nm).
# Simulation Model for Correction and Modeling of Probe Head Errors in Five-Axis Coordinate Systems
## 1 Introduction
Assuming that deflection can be described similarly to the definition of coordinates in a spherical coordinate system, the final probe head error function (PEF) can be written in the following form in Equation (1) [13]:  
\[\mathrm{PEF}\,=\,(\alpha,\,\beta,\,\mathrm{PE}) \tag{1}\]  
The standard used during the model preparation stage is the calibration sphere which fulfills the requirements formulated in [13]. The sphere is measured in evenly distributed points on the upper hemisphere of the standard. These points define the nodes of the reference grid of the model, in which probe head errors are determined empirically through measurements. The arrangement of the points can be described using angles (\(\alpha\), \(\beta\)). The sphere is measured at least 10 times, and on the basis of the acquired results the mean value of the PEF at each point is calculated, as well as standard deviation associated with it. Then, a \(t\)-distribution described by \(\overline{x}\), \(s\), \(\
u\) parameters is assigned to each grid node, where \(\overline{x}\) is the mean value of the distribution, equal to the mean value of the PEF, \(s\) is the dispersion equal to the calculated standard deviation, and \(\
u\) is the number of degrees of freedom equal to the number of measurements minus one. When the direction is different than the one for which errors were determined experimentally, the bilinear interpolation adapted for the spherical system must be used in order to determine the values describing the probability distribution. The value of the PE for the chosen probing direction is simulated using the Monte Carlo method. Since the PE can be treated as a vector with the same direction as a probing direction, it is possible to calculate its effect on the coordinates of measured points. The probing errors along axes \(x\), \(y\), \(z\) of a machine's datum system (PE\({}_{x}\), PE\({}_{y}\), PE\({}_{z}\)) can be expressed as follows:  
\[\mathrm{PE}_{x} = \mathrm{PEF}\times nx \tag{2}\] \[\mathrm{PE}_{y} = \mathrm{PEF}\times ny\] (3) \[\mathrm{PE}_{z} = \mathrm{PEF}\times nz \tag{4}\]  
Figure 1: Construction of the neural network used for probe error modeling. \(\alpha\): probe deflection angle; PEF: Probe Error Function. where:  
\(nx\), \(ny\), \(nz\)--approach direction cosines  
PEF--value of probe head error function  
The experiment described in [23] was conducted to check the number of points that should be included in the reference grid. As a result, it was found that the model based on 46 points accurately describes the probe errors. More details regarding this model can be found in [13]. The modules presented above served as a starting point for adapting the probe error function to describe the field of errors of the probe heads utilized on five-axis coordinate systems.
## 3 Autonomous Driving Sensor Data-Based C-ITS Environment Architecture
### Implementation--Road Surface Condition Recognition Example  
On cracked roads, the vehicle vibrates while driving, reducing the passenger's ride comfort and increasing the risk of accidents. Therefore, continuous monitoring and management of cracked roads are required. As the acceleration sensor IMU provides acceleration in the x, y, and z axes, the degree of the vibration of the vehicle can be determined through the sensor value analysis. Therefore, the traffic center can check the road surface condition using the vehicle IMU sensor data. There is an example that recognizes the road surface condition using the built system.  
1. It delivers the IMU data collected from the vehicle to the IMU topic of Kafka.
2. The data in MariaDB from the IMU topics are stored. At the same time, Spark uses the streaming function to find data with significant fluctuations in the y- and z-axis values in real time.
3. When abnormal data are detected, Spark delivers information on the location of the occurrence to Kafka.
4. The service environment collects messages delivered from Kafka-service topics.
5. The information collected for road management and monitoring is displayed on a web-based map in real time.  
The result of recognizing the road surface condition using the built system is shown in Figure 14. Currently, the driving information and the IMU sensor data are used. However, if an advanced object detection algorithm is applied to the vehicle control, the data in the object table of the RDBMS and the sensor data are fused to obtain the accurate road condition information.  
Figure 14: **(a)** Location information transmitted when Spark detects abnormal data; **(b)** web-based visualization of the messages delivered to the central LDM.
# Adaptive DCS-SOMP for Localization Parameter Estimation in 5G Networks
## 3 System Model
Finally, the received signal for subcarrier \(n\) and transmission \(g\) can be expressed as follows:  
\[\mathbf{y}^{(g)}[n]=\mathbf{H}[n]\mathbf{x}^{(g)}[n]+\mathbf{w}[n], \tag{4}\]  
where \(\mathbf{x}^{(g)}\) represents the signal with the transmitted data symbols, and \(\mathbf{w}\) denotes a Gaussian noise vector with a zero mean and variance \(\sigma^{2}\).
## 4 Rotor Blade Experimental Results for Various Non-Nominal Conditions  
An analysis of 380 MW steam turbine LP last stage rotor blade vibrations in various non-nominal conditions and condenser pressures was carried out.  
The rotor blade times of arrival were measured by two inductive sensors installed in the casing at 15 deg. to each other [6; 27] (Figure 2).  
Test alerts of excessive blade vibration and flutter were provided by an NI CompactRIO controller. The data acquisition system used an NI PXI-1065 computer equipped with PXIe-6358 and PXIe-6612 modules running a LabView application [27]. The time-of-arrival measurements were performed using a 100 MHz clock and 32-bit counters of a PXIe-6612 module, and the TTL pulses were generated by an in-house condition unit [6].  
Three measurements were carried out for various condenser pressures and the powers are presented in Table 1. In measurement 1, the condenser pressure changed from 10.4 kPa (368.7 MW) to 23.6 kPa (365 MW) and next decreased to 11.2 kPa (361.4 MW). In measurement 2, the condenser pressure was lower than in measurement 1, starting from 7.4 kPa (299.1 MW) to 19 kPa (300 MW) and decreasing to 7.4 kPa (298.3 MW). In measurement 3, the condenser pressure was lower than in measurements 1 and 2, starting from 5.9 kPa (219.5 MW) to 18.7 kPa (219 MW) and decreasing to 5.9 kPa (218 MW).  
Blade displacements vary with pressure changes in the condenser, increasing with higher condenser pressure and decreasing with lower pressure. By using the nonlinear least squares (L-M) algorithm it is possible to calculate vibration amplitude variations in time for various condenser pressures. Figure 3a shows the amplitudes and condenser pressures  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Measurement** & **Condenser Pressure** & **Power** \\ \hline
1 & 10.4 kPa–23.6 kPa–11.2 kPa & 368.7 MW–365 MW–361.4 MW \\ \hline
2 & 7.4 kPa–19 kPa–7.4 kPa & 299.1 MW–300 MW–298.3 MW \\ \hline
3 & 5.9 kPa–18.7 kPa–5.9 kPa & 219.5 MW–219 MW–218 MW \\ \hline \hline \end{tabular}
\end{table}
Table 1: Blade measurements.  
Figure 2: Sensors mounted in the casing of steam turbine LP last stage [27].  
(violet line) of blades 9 (green points) and 14 (brownish-red points) in time, for measurement 2. Out of the 53 blades, blade 9 had the highest amplitude and blade 14 had the lowest one. The blade amplitudes in measurement 2 were similar to those of measurement 1. Figure 3b shows the amplitudes and condenser pressures of two blades in time, for measurement 3. The green points are the displacements of blade 45, whereas the brownish-red points are the displacements of blade 45. Blade 45 had the highest amplitude and blade 41 had the lowest.  
In both cases (Figure 3a,b), the blade amplitudes increased with higher pressure in time.  
Figure 3: (**a**) Amplitudes of blades 9 and 14 versus time and condenser pressure variations in measurement 2. (**b**) Amplitudes of blades 45 and 31 versus time and condenser pressure variations in measurement 3.
# An Efficient Data Retrieval Parallel Reeb Graph Algorithm
## 8 Applications
#### Cutting System Curves.  
Reeb graphs can also be used to determine the so-called _cutting system_. A cutting system for a connected, closed, orientable surface \(M\) of genus \(g\) is a collection of unordered disjoint simple closed loops embedded in \(M\) whose complement \(M\backslash(l_{1}\sqcup\cdots\sqcup l_{g})\) is a sphere with 2 g boundary components [85]. Segmenting a surface along a cutting system curve yields a genus zero surface with multiple boundary components. Hence, this can be used to aid in mesh parametrization. See, for instance, [86] and the references therein. Here, we describe a Reeb graph-based algorithm to obtain a cutting system. The algorithm is illustrated in the Figure 20 and it goes, as follows:  
1. Let \(T\) be a spanning tree of \(R(M,f)\) and consider the edges \(e_{1},...e_{g}\) in \(R(f)\backslash T\).
2. Select an interior point in \(e_{i}\), for \(0\leq i\leq g\).
3. Each interior point selected in the previous step determines a loop \(l_{i}\), which can be obtained using the Reeb graph algorithm we described here.  
The steps of the cutting system algorithm are described in Figure 20.  
Figure 20: The steps of the cutting system algorithm. (1) mesh \(M\) with a scalar function defined on it are given. (2) The Reeb graph \(R(M,f)\) is computed. (3) We compute a spanning tree of \(R(M,f)\). (4) We select all edges in the graph that do not belong the spanning tree we computed in (3) and then we select an interior point on each one of these edges. (5) For each interior point, we used our augmented Reeb graph algorithm to compute the circle that corresponds to it on the mesh.  
Figure 21 shows multiple examples of cutting system curves on triangulated surfaces.
# An Experimental Validated Control Strategy of Maglev Vehicle-Bridge Self-Excited Vibration
## 5 Numerical and Experimental Validation
### Numerical Validation  
To obtain a reliable conclusion, the conditions of the magnetic levitation project should be simulated as precise as possible. In maglev engineering, the direct component of the acceleration signal is not absolute zero. Hence, its direct component is set as 0.2 m/s\({}^{2}\). The noise is set as 0.2% when compared to the maximum amplitude of the signal. Besides, the overall nonlinear dynamic model with details, which is shown in Figure 10, is adopted to carry out the numerical simulation. As for this model, ten modules (Due to the limit of Figure's size, only three modules is shown as follow.) are included and distributed along the length direction of the vehicle symmetrically. Each module consists of two levitation units.  
During the process of simulation, the nonlinear character of the levitation system, the saturation of the control voltage of the electromagnets, the dynamic responses of air-springs, the real-time estimation of the signals of the electromagnet's displacement and the coupling between the adjacent levitation units are all considered.  
In this subsection, the parameters of controller are set as \(k_{\mathrm{p}}=1000\), \(k_{\mathrm{d}}=30\), \(k_{\mathrm{a}}=0.4\), and \(k_{\mathrm{B}}=30\). The parameters of bridge are set as \(k_{\mathrm{B}}=0.005\), and \(\omega_{\mathrm{B}}=2\pi\times 13\mathrm{Hz}=81.68\) rad/s.  
Figure 9: The block diagram of maglev vehicle-bridge system with estimator.  
The modal frequency \(\omega_{\text{B}}\) = 81.68 rad/s selected in this case belongs to the unstable interval 67.3-118.7 rad/s. As we expected, the self-excited vibration starts to grow up at \(t\) = 2 s. According to Figure 11b, it can be seen that the amplitude of the electromagnet's acceleration is up to 2 m/s\({}^{2}\), which indicates the electromagnet vibrates violently. The vibration of electromagnet will be transferred to the vehicle, which degrades its ride comfort. The fluctuation of electromagnet displacement is about 0.5 mm. The fluctuation impacts the durability and safety of the bridge.  
According to Figure 11d, it can be seen that the estimated displacement of electromagnet is unfaithful when \(t\) < 2 s, which is due to the displacement estimator's transient response. In light of Figure 11e, when the transient response disappears, the estimated signal is converged to the real signal of electromagnet's displacement. To show its validity, the improved control scheme is activated at \(t\) = 4 s. It can be seen that the vibration amplitudes of all states rapidly decay to zero. When \(t\) > 4.5 s, the self-excited vibration disappears absolutely.
# A Simple and Robust Equalization Algorithm for Variable Modulation Systems
## 2 Conventional Equalization Algorithm
### Mcma  
The MCMA is proposed to solve the problems of the CMA phase error. The cost function of the MCMA can be expressed as:  
\[J(k)=E\bigg{[}\Big{(}y_{R}(k)^{2}-R_{2,R}\Big{)}^{2}\bigg{]}+E\bigg{[}\Big{(}y _{I}(k)^{2}-R_{2,I}\Big{)}^{2}\bigg{]}, \tag{5}\]  
where the real correction parameter \(R_{2,R}\) and imaginary correction parameter \(R_{2,I}\) are defined as:  
\[R_{2,R}=E\Big{[}|a_{R}(k)|^{4}\Big{)}\Big{]}/E\Big{[}|a_{R}(k)|^{2}\Big{]}, \tag{6}\]  
\[R_{2,I}=E\Big{[}|a_{I}(k)|^{4}\Big{)}\Big{]}/E\Big{[}|a_{I}(k)|^{2}\Big{]}, \tag{7}\]  
Under different modulations, \(R_{2,R}\) and \(R_{2,I}\) have different values. The MCMA error signal can be expressed as:  
\[e(k)=y_{R}(k)\Big{(}y_{R}(k)^{2}-R_{2,R}\Big{)}+\mathrm{i}y_{I}(k)\Big{(}y_{I }(k)^{2}-R_{2,I}\Big{)}, \tag{8}\]  
The formula for updating the filter coefficients can be expressed as:  
\[\mathbf{w}(k+1)=\mathbf{w}(k)-\mu e^{*}(k)\mathbf{x}(k), \tag{9}\]  
The MCMA cost function contains amplitude and phase information. It can correct the phase of modulated signals with in-phase and quadrature equal amplitudes, such as QAM modulation, which cannot be corrected without in-phase or quadrature equal amplitudes. In addition, compared with the CMA, the MCMA could not significantlyimprove the steady-state error and convergence speed. It is also necessary to adjust the real and imaginary correction parameters to match different modulations for VM systems.
# A New Approach to Production Process Capability Assessment for Non-Normal Data
## 6 Research Results
### Distribution Normality Analysis and Transformation for Machine 1  
Figure 2 shows the distribution of treatment time for actual values and post-transformation values for machine 1. Meanwhile, Table 2 shows the basic statistics and results of the Jarque-Bera test for both real and transformed data. The \(p\)-value \(<0.0001\) for the actual data clearly indicates the non-normality of the data distribution. In addition, the skewness value (3.4916) shows their asymmetry, while the kurtosis value (15.6714) shows more extreme outliers than in a normal distribution (Figure 2).  
After applying the proposed power transform, the \(p\)-value (0.9403) for the transformed data clearly indicates that the data distribution is close to a normal distribution. In addition, the value of skewness (\(-0.0165\)) and the value of kurtosis (\(-0.1170\)) show no major asymmetry or outliers. The value of the transformation parameter for machine 1 is \(\lambda=-0.1775\). For the transformed values, the interval \(6-sigma\) is (1.4684; 3.1929), while after the inverse transformation we obtain (0.0014; 0.1149).  
After applying the proposed power transform, the \(p\)-value (0.2998) for the transformed data clearly indicates that the data distribution is close to a normal distribution. As with machine 1, the value of skewness (\(-0.0974\)) and the value of kurtosis (\(-0.9380\)) show no major asymmetry or outliers. The value of the transformation parameter for machine 3 is \(\lambda=-1.1468\). For the transformed values, the interval \(6-sigma\) is (\(-14.7956\); \(129.9583\)), while after the inverse transformation we obtain (\(-0.0878\); \(0.0143\)).
## 5 Discussion
### Limitations of the Study  
Despite the valuable insights gained from our study, several limitations need to be acknowledged, which may affect the generalizability and robustness of our findings. Firstly, this study was conceived as an exploratory investigation into the conceptual potential of our framework and not as a controlled user evaluation or performance benchmark. We aimed to collect early and in-depth reflections from prototyping experts to assess perceived advantages and challenges, rather than to measure the effectiveness of the framework in a statistically significant way. Consequently, our study relied exclusively on qualitative research methods, opting out of utilizing quantitative approaches. Although qualitative methods provide rich insights into participants' perceptions, the absence of quantitative data limits our ability to assess the impact of the framework with objective metrics. For instance, the inclusion of usability measurements or cognitive load assessments could have offered a more structured understanding of its practical value.  
Secondly, our sample size was relatively small, comprising only nine participants. This limited sample size could restrict the scope of our findings when it comes to drawing broader conclusions about the applicability of the framework. In addition, our study predominantly recruited participants from an academic background, resulting in limited representation from industry contexts. This imbalance in the demographics of participants may further constrain the generalizability of our findings.  
Thirdly, our study aimed to explore a conceptual framework rather than to test a fully developed system. As such, participants interacted with a predefined prototype that they had not personally designed. This unfamiliarity may have influenced their engagement and feedback, potentially introducing bias in their evaluation. Additionally, due to practical constraints, the study was conducted in a simulated environment rather than a real-world prototyping context, and the language learning scenario may not have been equally relevant to all participants.  
Lastly, while the prototype used for our study incorporated generative AI, it is important to note that our framework does not inherently require generative AI to be used in the application that is being prototyped. This mismatch between the framework's conceptual flexibility and the specific implementation presented may have shaped participants' perceptions and evaluations in unintended ways.
# The Oxidation of Isobutyraldehyde to Isobutyric Acid Using a Microenactor Technology System
## 3 Results and Discussion
### Pilot-Scale Experiment  
In pilot-scale experiment, the test is carried out using a custom-built microreactor system, and the production scale is scaled up. The process is shown in Figure 10, and the simplified structure of the reactor is shown in Figure 11.  
In the custom-built microreactor system, the main reactor consists of a cylindrical shell filled with microchannel tubing of a specific length. Referring to the Miprowa reactor design by [PERSON] (Koln, Germany), 3D-printed staggered inserts were placed inside the custom-built micro-channel. By stacking these inserts, efficient mixing of the fluid was achieved as it passed through.  
In terms of experimental parameters, the process was scaled up proportionally based on the optimal conditions identified at the laboratory-scale. The oxygen flow rate is 4 SLPM, the liquid flow rate is 20 mL/min, the operating pressure is 10 bar, the operating temperature is 20 \({}^{\circ}\)C, and the reaction time is between 1 and 3 h. The experimental results are shown in Figure 12.  
Based on the experimental results from Figure 12, similar results to the laboratory-scale experiment can be achieved within two hours of reaction time. After three hours of reaction, a conversion rate of 91.57% and a selectivity of 96.09% for isobutyric acid can be obtained.  
The results of the experiment demonstrate the effectiveness of the custom-built microchannel system. It achieves experimental outcomes comparable to those obtained with the Ehrfeld microreactor system, proving the advantage of microreactor technology in theory, which exhibits no scaling-effects.  
Figure 11: Structure of custom-built microreactor (**left**) and micro-channel (**right**).  
Figure 10: Flowchart for pilot-scale experiment.  
Under the current experimental conditions, a single custom-built microreactor system can achieve an annual production of 7-8 tons of isobutyric acid. The results of this study have been granted a patent in Taiwan. Additionally, a company has shown strong interest in this technology, and discussions for potential collaboration are currently underway. It is hoped that this research will serve as the foundation for establishing a production process for isobutyric acid at a scale of hundreds of tons per year.