esa-sceva/satcom-chunk-collection huggingface dataset:
# Phased System Reliability Modeling and Assessment for Construction of Lunar Scientific Base
## 2 A Phased Reliability Model for LSB Construction  
The LSB construction is divided into three main processes, as shown in Figure 1: (a) the fixed-point delivery of equipment on the lunar surface; (b) the lunar surface transport of equipment; (c) the docking and assembly of equipment on the lunar surface. Firstly, the lander transports the equipment required for the LSB operation from the earth orbit to the lunar orbit and further delivers it from the lunar orbit to the lunar surface landing site, thereby achieving a safe and stable landing on the lunar surface. Secondly, the lander converts from a landing attitude to a walking attitude or releases the lunar rover to complete the reliable and smooth transport of equipment from the landing site to the LSB destination.  
Afterward, the necessary docking and assembly of the equipment are performed at the LSB destination to form a functional module assembly [7]. The LSB construction is a long-term and gradual process that needs to be completed in stages according to the mission plan. Generally, the LSB construction process consists of \(m_{1}\) times of fixed-point delivery, \(m_{2}\) times of lunar surface transport, and \(m_{3}\) times of docking assembly.  
A phased reliability model for LSB constructions is developed in this study to facilitate the reliability assessment, as shown in Figure 2. The novel model makes the reliability confidence limits of each main process independent, thus avoiding correlation and simplifying the calculation. Several reasonable modeling approaches exist for the LSB construction, but the solution difficulty varies considerably between different models. For example, it is also applicable to model the subtask reliability for LSB construction, i.e., the overall construction consists of individual launch tasks in series, as shown in Figure 3. Since each launch task consists of three main processes: fixed-point delivery, lunar surface transport, and docking assembly, the confidence limits of reliability in each task are correlated (because the test samples of the three processes are related). This correlation will make the solution of mathematical models too complex.  
Figure 1: Schematic diagram of the three main processes of LSB construction. **(a)** The fixed-point delivery of equipment on the lunar surface, **(b)** The lunar surface transport of equipment, **(c)** The docking and assembly of equipment on the lunar surface.  
Figure 2: A phased reliability model for LSB construction. The letters A, B and C refer to the three main processes respectively, and the letters’ subscripts indicate the sequence number of subtasks.  
As mentioned above, reliability assessment for LSB construction or its major processes is impractical to directly achieve because of the complex construction processes. Instead, a feasible solution is fully utilizing the reliability test information at lower levels for a BBA. Aiming at this, this paper proposes reliability assessment methods for the three main processes and the overall construction process in Sections 3 and 4, respectively.
# Brake Light Detection Algorithm for Predictive Braking
## 2 State-of-the-Art
Moreover, previous daytime studies have focused on detecting brake lights from clear pictures captured of vehicles close to the research vehicle, in high traffic situations, mostly at a standstill. The detection method presented here is designed to detect the brake lights of moving vehicles at distances ranging from 5 m to 150 m. The approach is benchmarked, and proven to function in a multitude of scenarios, accurately performing at far distances. The presented approach is designed to be used in all daytime weather conditions, yet it also has drawbacks common to camera-based systems, such as lens flares caused by direct sunlight. The proposed algorithm and the gathered dataset will be published in an online repository to facilitate further research on the topic.
## 2 Materials and Methods
### Temperature-Dependent Luminescence Spectra Measurements  
Luminescent spectroscopy of the samples was performed on an installation consisting of a laser with a wavelength of 974 nm, a spectrometer (StellarNet, DWARF-Star) in the SWIR range of 1000-1700 nm, a spectrometer (BIOSPEC, LESA-01-BIOSPEC) in the visible range of 300-900 nm, and a thermal infrared camera (CEDIP, JADE MWIR SC7300M) for temperature measurements in the range from 24 to 44 \({}^{\circ}\)C [21]. Heating was carried out using a Primelab PL-R-basic H heated magnetic stirrer (Primelab, Mytischci, Russia) with 0.1 \({}^{\circ}\)C temperature heating sampling. Luminescence intensity ratio (LIR) was used to quantify the dependence of luminescence spectra on temperature in the SWIR range:  
\[\text{LIR}=\text{I}_{\text{Ho}}/\text{I}_{\text{Er}} \tag{2}\]  
where I is the integral luminescence intensity calculated as the area under the luminescence peak in the corresponding wavelength range. For SWIR the area was calculated under the peak in the 1100-1250 nm range for \(\text{Ho}^{3+}\)\({}^{5}\)I\({}_{6}\)\(\rightarrow\)\({}^{5}\)I\({}_{8}\) transition and the 1450-1650 nm range for \(\text{Er}^{3+}\)\({}^{4}\)I\({}_{13/2}\)\(\rightarrow\)\({}^{4}\)I\({}_{15/2}\) transition. In visible range we use luminescence in green range, 520-570 nm range, which corresponds to \(\text{Er}^{3+}\)\({}^{2}\)H\({}_{11/2}\), \({}^{4}\)S\({}_{3/2}\)\(\rightarrow\)\({}^{4}\)I\({}_{15/2}\), and in red range, 625-675 nm, which probably includes both the \(\text{Er}^{3+}\) and \(\text{Ho}^{3+}\) luminescence, transitions \({}^{4}\)F\({}_{9/2}\)\(\rightarrow\)\({}^{4}\)I\({}_{15/2}\) and \({}^{2}\)F\({}_{5}\)\(\rightarrow\)\({}^{5}\)I\({}_{8}\), correspondingly.  
The relative coefficient of temperature sensitivity (\(S_{r}\)) was calculated using the following formula:  
\[S_{r}=\frac{LIR_{T1}-LIR_{T2}}{T_{2}-T_{1}}*100\% \tag{3}\]
## 4 Methodology for the Performance Analysis of the Physical Layer Proposals  
The methodology of this study is based on standardized procedure, test setup, and metrics. Moreover, the evaluated channel disturbances are divided into noises defined by ETSI and a selection of noises caused by distributed energy resources (DER) obtained from field trials. Additional details about the methodology can be found in a previous work from the authors [10].
## 4 Conclusions  
The findings of this study collectively confirm that the integration of process-informed fiber orientation data into a multi-scale modeling framework enables a highly accurate prediction of both static and dynamic mechanical behavior in SFRP components. The simulation results, validated through tensile and compressive testing as well as dynamic modal analysis, demonstrate strong agreement with experimental observations, particularly in terms of fracture location, strength values, and vibrational response.  
To quantitatively evaluate the accuracy of the simulations, error metrics were calculated for both static and dynamic tests. For tensile strength predictions across three fiber orientations (0\({}^{\circ}\), 45\({}^{\circ}\), and 90\({}^{\circ}\)), the mean absolute error (MAE) and root mean square error (RMSE) were approximately 6.67 MPa and 7.07 MPa, respectively. In dynamic hammering tests, the estimated MAE and RMSE for frequency response in the X, Y, and Z directions were approximately 1.0 dB/g, further validating the model's predictive capability.  
The ability to reflect anisotropic stiffness characteristics, influenced by fiber orientation and molding conditions, significantly enhances the reliability of structural simulations. This approach not only supports the prediction of performance variations due to changes in fiber content, gate position, and part geometry, but also facilitates the design and optimization of composite materials tailored to specific functional requirements. Therefore, the proposed methodology provides a robust and scalable foundation for advancing the predictive engineering of fiber-reinforced polymer components in automotive and other high-performance applications.  
Formal analysis, Writing--original draft preparation: Y.S.C.; Resources: B.J.; Data curation: J.M.; Supervision, Writing--review and editing: K.K. and H.L.; Project administration: Y.S.C. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government (MOTIE) (No. 20224000000040, Innovation Research Center for Giant Wind Turbine) and Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government (MOTIE) (No. 20228520020050, Development of 25 MW Offshore Wind Turbine Reference Model and Conceptual Design Technology).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Author [PERSON] was employed by the company TRINITY Engineering Co., Ltd. and Author [PERSON] was employed by the company Daeheung R&T. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Problem Formulation and Modeling
### Geometric Properties of Trajectory  
Therefore, by analyzing its geometric properties, the problem can be converted into a convex function that can be solved. Use a classic math trick, we multiply both sides of Equation (9) by \(r^{2}\) and then substitute it into Equation (4); the following equation can be obtained.  
\[(x_{f}-(x(t)-\frac{b}{2\theta}))^{2}+(y_{f}-(y(t)-\frac{d}{2\theta}))^{2}= \frac{b^{2}+d^{2}}{4\theta^{2}} \tag{10}\]  
where \(b\) and \(d\) are, respectively,  
\[b =V(\mu\cdot sin(\theta(t))+\lambda(\theta_{f}-q(t))\cdot cos( \theta(t))) \tag{11a}\] \[d =V(-\mu\cdot cos(\theta(t))+\lambda(\theta_{f}-q(t))\cdot sin( \theta(t))) \tag{11b}\]  
To simplify the expression in Equation (10), we define \(x_{f}\triangleq x(t_{f}),y_{f}\triangleq y(t_{f}),\theta_{f}\triangleq\theta (t_{f})\), and \(q(t)=tan^{-1}\frac{y_{f}-y(t)}{x_{f}-x(t)}\).  
The conventional method is to select \(t_{i}\in(t_{0},t),i=1,2,3\) before \(t\) at the current moment to obtain Equation (10) by solving a system of nonlinear equations, solving for \(x_{f},y_{f},\theta_{f}\), three unknowns. However, the problem is, in fact, that it is not possible to obtain a convergent solution by directly applying [PERSON]'s method, L-M solver, etc. (experimental test), and it is necessary to first transform the solution problem into a reasonable optimization problem (preferably a convex optimization problem).  
Continuing to work on Equation (10), it is obvious that for any selection of \(t\in\left(t_{0},t_{f}\right)\), there is a parametric circle with the coordinates of the center of the circle \((x_{c},y_{c})\), where \(x_{c}=x(t)-\frac{b}{2\theta}\) and \(y_{c}=y(t)-\frac{d}{2\theta}\), and the radius of the circle is \(r=\frac{\sqrt{b^{2}+d^{2}}}{2|\theta|}\), and the circle intersects the trajectory line at \((x_{t},y_{t})\) and \((x_{f},y_{f})\). Literally, if the parameter circle corresponding to a sampling point intersects the trajectory line at the desired endpoint, then the parameter circle corresponding to two different sampling points must intersect at the desired endpoint together. Thus, its geometric properties can be generalized as follows.  
**Property 1**.: _The parameter circles of any sample points on the trajectory line are intersected at the same point._  
**Property 2**.: _The same point at which the parametric circles intersect is the desired final state position._  
To characterize the aforementioned properties, a representative trajectory is analyzed with sampling instants defined at \(t=0.001,15.001,30.001\) s. The constructed parameter circles in Figure 4 demonstrate two critical geometric features: (i) All circles intersect the trajectory precisely at both the sampled instants and the terminal waypoint \((x_{f},y_{f})\); (ii) these circles exhibit a common intersection point at \((x_{f},y_{f})\), confirming the geometric invariance predicted by Property 2.
# The Key Role of Laser Ultrasonics in the Context of Sustainable Production in an I 4.0 Value Chain
## 4 The Key Role of LUS in a Smart Manufacturing Production Site
### Operating Principle
#### 4.1.1 Ultrasonic Velocity  
The propagation speed of ultrasonic waves (especially longitudinal \(c_{L}\) and shear waves \(c_{T}\)) depends on the density and the stiffness tensor [108]. Assuming a polycrystalline, isotropic material, with a small grain size in relation to the sample size, the following rela  
Figure 1: Schematic representation of the operating principle of a LUS System.  
tionships between the speed of sound, density and elastic constants (\(K,S,\
u\)) for longitudinal and transverse waves can be assumed:  
\[c_{L}=\sqrt{\frac{K}{\rho}\cdot\frac{1-\
u}{(1+\
u)(1-2\
u)}} \tag{1}\]  
for the sound velocity of longitudinal waves and  
\[c_{T}=\sqrt{\frac{S}{\rho}} \tag{2}\]  
for that of transverse waves. \(K\) is referred to as the Compression Module, \(S\) is the Shear Module, and \(\
u\) is the Poisson ratio. In the simplest case, the propagation speed can be determined by ToF measurements. For a sample of thickness \(h\), the propagation velocity can be derived from the time required for the wave to be reflected at the back of the sample and to be returned:  
\[v=\frac{2h}{\Delta t} \tag{3}\]  
If the density \(\rho\) of the inspected material is known, the Young's modulus (via known relationships with the compressive and shear moduli) or [PERSON]'s ratio \(\
u\) can be determined very accurately over temperature, by measuring the speed of sound.  
Since polycrystalline metals generally exhibit preferential grain orientations as a result of the manufacturing process (solidification and preferential dendrite growth or forming processes), it is of particular importance to be able to determine the predominant orientation.  
Due to the high sensitivity of laser ultrasonic measurements with respect to the velocity of sound, individual entries of the elastic stiffness tensor can further be determined. Since the propagation velocity differs depending on the direction of propagation, the preferred direction can be determined using reference crystals. Conversely, orientation distribution coefficients (ODC) can also be calculated from ultrasonic signals.  
Recrystallization phenomena, respectively the recrystallized fraction, can similarly be measured by measuring the changes in texture, and in turn the change in ultrasonic velocity. Since recrystallization is preceded by a certain strain and thermal activation, the changes in the measured sound velocity can be assigned to the recrystallized fraction via the Johnson-Mehl-Avrami (JMAK) function.  
Especially for phase transformation or allotropic transformations of metals, a very precise prediction can be obtained with this measuring system. Since the acoustic velocity differs strongly depending on the crystal structure, the phase composition rule (lever rule) can provide an exact determination of the predominant fractions [109]. The speed of sound is also changed considerably during a transition from ferromagnetic to paramagnetic material (Curie temperature).
## 2 Materials and Methods
### Analysis of Volatile Compound Measurement by Gas Chromatography-Mass Spectrometry (GC-MS)  
Volatile compounds were analyzed using the headspace solid-phase microextraction (HS-SPME) technique described previously by [PERSON] et al. [16]. Extraction was performed using SPME fiber and analysis. The sample (5 mL) was placed in a 10 mL glass vial. The vial was sealed with a Teflon-faced septum cap, and the sample was pre-conditioned at 40 \({}^{\circ}\)C for 15 min. Microextraction lasted for 40 min at 40 \({}^{\circ}\)C with stirring (1100 rpm). For desorption, the fiber was inserted into the GC-MS injector port for 10 min (3 min in the splitless mode). The fiber was withdrawn into the needle and injected into the injection port of the GC-MS at a temperature of 220 \({}^{\circ}\)C for 3 min. GC-MS analysis was conducted on a Varian CP-3800/Saturn 2000 (Varian, Wallnut Creek, CA, USA) equipped with a ZB-5 MS capillary column (30 m \(\times\) 0.25 mm inner diameter (i.d.) \(\times\) 0.25 \(\upmu\)m film thickness). The initial oven temperature was kept at 50 \({}^{\circ}\)C, raised to 130 \({}^{\circ}\)C at a rate of 4 \({}^{\circ}\)C min\({}^{-1}\), subsequently increased to 180 \({}^{\circ}\)C at 10 \({}^{\circ}\)C min\({}^{-1}\), and finally increased to 280 \({}^{\circ}\)C at a rate of 2 \({}^{\circ}\)C min\({}^{-1}\). Helium was used as a carrier gas at a flow rate of 1.0 mL min\({}^{-1}\). The samples were manually injected in a split mode (1:10). Mass spectra were obtained in an electronic ionization (EI) mode of 70 eV, with a scan range of _m/z_ 35-550. After each extraction was completed, the SPME fiber was placed in the injection port of GC-MS at 220 \({}^{\circ}\)C for 15 min to ensure that the volatiles were completely eluted to prepare for the subsequent extraction. A mixture of aliphatic hydrocarbons soluble in methanol in the range from C-5 to C-23 was used as a standard for determining retention indexes. Volatiles were identified by comparing the mass spectra of the compounds obtained experimentally, and the mass spectra were available in the National Institute of Standards and Technology (NIST 14) database. Identification of compounds is based on Kovats retention time indices, which allow you to assign a specific smell to a given compound.
## 4 Discussion  
The model proposed in this study can be used as an early warning system that will strengthen waterwork network management. When the prediction results show that the MNF increases quickly, we can take preemptive actions promptly--such as repair and replacement--so as to minimize losses, e.g., from water leakages from external forces such as road works.  
It can be seen that the accuracy of the LSTM-MNF model's predictions with the YD 2, 3, and 4 data was better than the MLP-MNF model. The MAEs of the LSTM-MNF model for YD 2, 3, and 4 were 0.416, 0.751, and 0.532, respectively. The MSEs of the LSTM-MNF model for YD 2, 3, and 4 were 0.259, 1.01, and 0.419, respectively. The MAPEs of the LSTM-MNF model for YD 2, 3, and 4 were 6.231, 3.682, and 3.806, respectively.  
Figure 12: MNF prediction with the test data from YD 4 with the LSTM model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{4}{c}{**YD 2 Data**} \\ \cline{2-5}  & **MAPE** & **MAE** & **MSE** & **R-Squared** \\ \hline MLP-MNF & 7.572 & 0.372 & 0.612 & 0.868 \\ LSTM-MNF & 6.231 & 0.416 & 0.259 & 0.907 \\ \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{4}{c}{**YD 3 Data**} \\ \cline{2-5}  & **MAPE** & **MAE** & **MSE** & **R-Squared** \\ \hline MLP-MNF & 4.020 & 0.849 & 1.135 & 0.257 \\ LSTM-MNF & 3.682 & 0.751 & 1.010 & 0.488 \\ \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{4}{c}{**YD 4 Data**} \\ \cline{2-5}  & **MAPE** & **MAE** & **MSE** & **R-Squared** \\ \hline MLP-MNF & 4.253 & 0.581 & 0.548 & 0.637 \\ LSTM-MNF & 3.806 & 0.532 & 0.419 & 0.733 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation results for three sectorized DMAs (YD 2, YD 3, YD 4).  
There are many improved versions of these artificial intelligence models, such as Bi-directional Long Short-Term Memory networks (Bi-LSTMs) and Gated Recurrent Units (GRUs). These and other new methods can be used for comparison with the model proposed for improved prediction.
# Experimental Validation of Ellipsoidal Techniques for State Estimation in Marine Applications
## 2 Autonomous DDboats
### Modeling of the DDboats
#### 2.3.1 Identified Model-Based Representation of the Effective System Inputs  
To account for the knowledge of the control strategy (4)-(9) described in the previous subsection, the effective system inputs in (10) can be specified as  
\[u_{\wp}^{(i)}(t)=p_{1}^{(i)}\cdot\left(\mathbf{v}^{(i)}(t)\right)^{2}+p_{2}^{(i)} \cdot\left(u_{1}^{(i)}(t)+u_{\rm r}^{(i)}(t)\right)+p_{3}^{(i)}\cdot\left(\left( u_{1}^{(i)}(t)\right)^{2}+\left(u_{\rm r}^{(i)}(t)\right)^{2}\right) \tag{12}\]  
and  
\[u_{\mathbf{\phi}}^{(i)}(t)=p_{4}^{(i)}\cdot\left(u_{1}^{(i)}(t)-u_{\rm r}^{(i)}(t) \right)+p_{5}^{(i)}\cdot\left(\left(u_{1}^{(i)}(t)\right)^{2}-\left(u_{\rm r}^{ (i)}(t)\right)^{2}\right)\, \tag{13}\]  
where the parameters \(p_{m}^{(i)}\), \(m\in\{1,\ldots,5\}\), are computed from an offline least-squares identification based on the GPS-based position measurements \((x_{1,{\rm m}}^{(i)}(t)\), \(x_{2,{\rm m}}^{(i)}(t))\), the compassinformation for the ships' headings \(\phi_{\text{m}}^{(i)}(t)\), and the experimental control signals \(u_{\text{l}}^{(i)}(t)\) and \(u_{\text{r}}^{(i)}(t)\). Required derivatives are determined numerically by using an explicit Euler scheme with a fixed sampling time \(T_{\text{s}}=1\,\text{s}\).
## 2 Materials and Methods
### Measurement Procedures  
Alginate impressions (Tropical Zhermack, Badia Polesine, Italy) were taken and immediately poured with dental stone (Elite Rock-Zhermack). Intra-arch dimensional dental measurements were recorded using a digital caliper (Mitutoyo, Tokyo, Japan) on the plaster models and 3D reverse engineering software (Geomagic Design X version 2022, Oqton, Los Angeles, CA, USA) on the intraoral scans.  
The intra-arch measurements included tooth heights (H) and widths (W), overjet (O), and overbite (OB). Additionally, the arch width was examined at the first permanent upper molar (AWU6) and the first permanent lower molar (AWL6). The arch depth was measured at the first permanent upper molar (ADU6) and the first permanent lower molar (ADL6).  
Tooth heights were measured from the incisal edge or cusp tip to the zenith point. Tooth widths, which are mesiodistal dimensions, were the largest dimensions from the mesial contact point to the distal contact point, parallel to the occlusal plane. Overjet was the distance from the middle of the incisal edge of the right maxillary incisor to the labial surface of the opposing mandibular incisor, parallel to the occlusal plane. Overbite was the vertical distance from the marked incisal edge of the right maxillary central incisor overlapping the labial surface of the right mandibular central incisor to the incisal edge of the mandibular incisor. The arch width was the distance from the mesiobuccally cusp tip of the right maxillary molar to the mesiobuccally cusp tip of the left maxillary molar. The arch depth was examined by drawing a line from the outermost plane of the two central incisors to the distal plane of the two first molars and measuring the distance from this line to the midline.  
Regarding blinding, three individuals measured dimensions of teeth in plaster and digital models and another examiner compared results. Bland-Altman analysis was employed to examine the agreement between plaster models and intraoral scans. The plot featured a scatter diagram of the differences against the averages of the two measurements. Horizontal lines indicated the mean difference and the limits of agreement, defined as the mean difference \(\pm\) 1.96 times the standard deviation of the differences. Calculations were carried out for the mean difference, the standard deviations of the differences, and the limits of agreement between the plaster models and intraoral scans.  
Each examiner re-measured tooth heights and widths after 14 days to assess intra-rater correlation coefficient (n = 10 patients). The values of tooth heights and widths measured by each examiner were evaluated for inter-rater correlation coefficient (n = 10 patients). The intraclass correlation coefficient (ICC) ranges from 0 to 1, based on the 95% confidence interval of the estimate. Values less than 0.5, from 0.5 to 0.75, from 0.75 to 0.9, and greater than 0.9, respectively, indicate poor, moderate, good, and excellent reliability [10].