esa-sceva/satcom-chunk-collection huggingface dataset:
# Resonant Laser Ionization and Fine-Structure Study of Silver in an Ablation Plume
## 3 Materials and Methods
### Dye Laser Systems  
Both the transitional wavelengths were generated using dye lasers. The first step was provided by a Quantel TDL50 dye laser pumped with a 532 nm (1.1 W @ 10 Hz) beam from a Quantel YG 580 laser. The dye solution used was sulforhodamine 640 (0.3 g/L for the oscillator and 0.075 g/L for the amplifier) with methanol solvent [22]. This dye laser was tuned at 656.326 nm; then. the resulting laser beam was frequency doubled with a second harmonic generation (SHG) crystal, which produced UV light at 328.163 nm. After the frequency doubling, the fundamental (656.326 nm) and the second harmonic (328.163 nm) were spatially separated by passing through a prism. The fundamental beam was sent to a HighFinesse WS7 wavemeter to continuously monitor the wavelength.  
The second step was provided by a Lambda Physik FL2002 dye laser pumped with 355 nm (0.5 W @ 10 Hz) from a Quantel YG 980. The dye solution used was Stilbene 420 (0.25 g/L both for the oscillator and the pre-amplifier) with methanol solvent [22]. The system was tuned at 421.402 nm. The two pump lasers and the ablation laser were synchronized and it was possible, therefore, to experimentally optimize the time delay between the laser pulses.  
Figure 2: Experimental set-up for the laser photoionization study of Ag. PUMP 1 and PUMP 2 represent the pump Nd:YAG lasers, while DYE 1 and DYE 2 represent the dye lasers, respectively. SHG is the second harmonic generation crystal and TOF-MS represents the time-of-flight mass spectrometer.
## 5 Discussion of Results
### Input and Dissipated Energy  
From Figure 18, it is seen that the OB dissipates all the input energy by modal damping. In reality, part of the input energy would also be dissipated by flexural plastic hinges forming at structural members ends, which have not been modeled in the FEM of the building. The effectiveness of both TMD solutions is also proven by the energy-time graphs shown in Figure 18. Actually, TMD1 and TMD2, despite inducing an increase in the total input energy, halve the energy dissipated by modal damping. From Figures 19 and 20, it can be seen that the mean force and the relative displacements are similar for the devices of TMD1 and TMD2. Thus, these devices dissipate similar energy amounts.  
Among the intervention strategies considered in the presented case study, BIS is the most effective in reducing stresses arising in the structural members due to seismic actions. Indeed, \(\frac{M_{Eq}}{M_{Rd}}\) ratios are lower than one tenth of those of the OB, and the highest is 0.275 (see Tables 11 and 12). This is due to the fact that BIS reduces the input energy and dissipates most of it by RI's hysteretic cycles. Hence, the energy dissipated by the building through modal damping is reduced to at least one tenth of the OB. Despite the fact that the force-displacement cycles of RIs (Figure 21) are narrower than those of LRIs and VLDs (Figures 19 and 20), the energy dissipated by RIs is greater than that dissipated by LRIs and VLDs because RIs are more numerous than LRIs and VLDs.  
TMD may also be a valid and efficient strategy for the retrofit of existing buildings as an alternative to dissipative braces [47; 48] and base isolation [49; 50], which often require interventions on the foundations and on the superstructure, making these solutions too expensive or technically unfeasible.
## 4 Methods
### Site Suitability Evaluation Based on Multi-Factors
We comprehensively use the \(EGSI\) to evaluate the geological conditions and determine how they might affect the engineering site. The \(EGSI\) quantifies the suitability of the conditions for construction and operation of the RI facility at a given site [47]. Using the \(EGSI\) methodology is an improvement on the existing siting process, which relies heavilyon subjective data and impressions. We can calculate the \(EGSI\) on large spatial scales (to identify RI locations) and small spatial scales (to identify where certain instruments should be located within the RI site) [48]. If the geotechnical parameters are sound, we can reasonably expect the long-term, stable, and efficient operation of the RI facilities. The purpose of normalization is to eliminate the unbalanced contributions of different factors to the ultimate result in order to make the data statistically comparable. Using the \(EGSI\), the CSNS site was evaluated for suitability zonation, geohazards susceptibility, basement stability, standard penetration, weathered rock thickness, quaternary thickness, main lithology, and the slope gradient (Figure 4). Figure 4: Geological suitability zonation at the CSNS site for different indices, such as (**a**) suitability zonation, (**b**) geohazards susceptibility, (**c**) basement stability, (**d**) standard penetration, (**e**) weathering thickness, (**f**) quaternary thickness, (**g**) main lithology, and (**h**) slope gradient at the CSNS site.
# Low Complexity List Decoding for Polar Codes with Multiple CRC Codes
## 3 Low Complexity SCL Decoding with Multiple CRC Codes  
As the list size \(L\) increases, the performance of the SCL decoding is improved, but the operational complexity also increases. Actually, in the LLR-based SCL decoding under consideration, the total operational complexity is the sum of \(\text{C}_{\text{S}}^{i}\) as in Equation (5), where each contributing term at \(\text{C}_{\text{S}}^{i}\) is linearly or slightly superlinearly related with \(l_{i}\). Therefore, the goal of this work is to reduce the operational complexity of the SCL decoding by managing the temporary list size lower by the aid of multiple CRC codes.  
Previous research utilized multiple CRC codes to reduce the memory and time complexity of the corresponding SCL decoding [21; 23]. Although multiple CRC codes were used to reduce the operational complexity in [22], in the complexity evaluation, the temporary list size was not counted and only uniform (equal-length) message partitioning was considered.  
However, we consider the operational complexity reduction via nonuniform message partitioning and temporary list size management. If we can keep the temporary list size small, the overall complexity can be reduced. In this paper, in order to minimize the complexity effectively, the positions of CRC codes are optimized and a modified SCL decoding is employed.
# Stable Throughput Analysis of Heterogeneous Hybrid FSO/RF Networks with Cognitive Radio Capability
## 5 Stable Throughput of Economic System Model  
In this section, we assume that only the RO pair has licensed radio frequency RF \(f_{1}\). It is also assumed that only the HY transmitter has the CR and can access \(f_{1}\) with spectrum sensing. Since HY does not allocate \(f_{2}\), we set the transmission success probabilities \(s_{f_{2},j}\), \(s^{I}_{f_{2},j}\) to zero.
## 4 Experiment Setup and Evaluation Metrics
### Software  
We installed Python on the three IoT devices (DHT sensor + Raspberry Pi) and the fog device. Then, we installed the Circuit Python DHT Library on the three IoT devices to allow communication between the DHT11 sensor and Raspberry Pi. Next, we installed the cryptography library on the three IoT devices and fog device to perform the security operations. Algorithm 1 illustrates the authentication and authorization operations from IoT devices (DHT11 sensor + Raspberry Pi) to the fog device, and Algorithm 2 illustrates the authentication and authorization operations from the fog device to the IoT devices (DHT11 sensor + Raspberry Pi) and from the fog device to the AWS cloud.
# Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies
## 8 Conclusions
### Key Methodological Advancements  
The field has witnessed significant evolution in analytical approaches. The progression from fixed-parameter to random-parameter models has improved accounting for unobserved heterogeneity, while hierarchical Bayesian methods have enhanced the incorporation of spatial-temporal correlations. Advanced spatial analysis techniques, including geographically weighted regression, have revealed geographical patterns in crash occurrences.  
Data integration represents another crucial advancement. Combining police reports, hospital records, and insurance claims has addressed underreporting and misclassification issues. This multi-source approach, complemented by surrogate safety measures and traffic conflict analysis, provides alternatives when crash data are limited.  
Machine learning and AI applications have uncovered complex, nonlinear relationships in crash data. Real-time crash risk prediction, utilizing streaming sensor and telematics data, enables proactive safety management. Novel severity analysis approaches, including latent class and mixed logit models, have improved injury outcome identification. Big data analytics has opened avenues for discovering previously unknown risk patterns, while methodological advances in addressing endogeneity and self-selection bias have produced more accurate intervention estimates.  
Despite these advances, autonomous and mixed traffic solutions emerge as the most promising frontier, alongside continuous data quality improvements.
# A Review on Data-Driven Condition Monitoring of Industrial Equipment
###### Abstract  
This paper presents an up-to-date review of data-driven condition monitoring of industrial equipment with the focus on three commonly used equipment: motors, pumps, and bearings. Firstly, the general framework of data-driven condition monitoring is discussed and the utilized mathematical and statistical approaches are introduced. The utilized techniques in recent literature are discussed. Then, fault detection, diagnosis, and prognosis on the three types of equipment are highlighted using a variety of popular shallow and deep learning models. Applications of these techniques in recent literature are summarized. Finally, some potential future challenges and research directions are presented.  
Keywords:data-driven; condition monitoring; motor; pump; bearing; fault detection; fault diagnosis; fault prognosis +
Footnote †: journal:
###### Abstract  
Background:To assess the cutting efficiency of two heat-treated endodontic files with a brushing motion in oval-shaped canals. **Methods**: A total of 10 intact lower molars with a single oval distal canal were selected and randomly divided into two groups according to the system used for shaping: 2 Shape and Hyflex CM. The instrumentation was performed in two parts: a first shaping according to the manufacturer's instructions and final shaping using 5 brushing motions on the distal, lingual, and buccal walls, avoiding the danger zones. Micro computed tomography (micro-CT) scans before and after the brushing motion were superimposed at all three levels: coronal, middle, and apical. Canal changes in buccalingual (ABL) and mesiodistal (\(\Delta\)MD) dimensions were measured. Data were statistically analyzed by repeated-measures analyses of variance and the student \(t\)-test (\(p<0.05\)). **Results**: For \(\Delta\)MD, no significant difference emerged between the 2 Shape and Hyflex CM in the apical and coronal thirds. However, the cutting efficiency was significantly greater with the 2 Shape in the middle third (\(p<0.05\)). Regarding \(\Delta\)BL, no statistically significant difference was detected between 2 Shape and Hyflex CM comparing the anatomical thirds, while 2 Shape had a significant higher cutting efficiency considering the total BL diameter (\(p<0.05\)). **Conclusions**: Under the limits of a vitro study, the shaping procedure with 2 Shape and brushing motion was more efficient than the Hyflex CM in the midroot levels in terms of \(\Delta\)MD, and in total canal space for ABL. Both files ensured an effective mechanical preparation.  
brushing motion; cutting efficiency; heat treatment; micro-CT; oval canal
# Mathematical Modeling of Capillary Drawing Stability for Hollow Optical Fibers
## 2 Mathematical Models
### Mathematical Model of Quartz Capillaries Drawing
The cited work presents the results of 24 measurements of the finished product radii during capillary drawing. The preform was made of Suprasil F300 glass with an outer radius \(r_{10}=14\times 10^{-3}\) m and an inner radius \(r_{20}=12\times 10^{-3}\) m. The preform was fed into the furnace at a constant speed, and the upper end of the tube was left open to the atmosphere. Feed speed \(v_{0}\) varied from \(2\times 10^{-3}\) m/min to \(8\times 10^{-3}\) m/min, drawing speed \(v_{L}\) varied from \(0.6\) m/min to \(1.2\) m/min at furnace temperatures of \(1900\,^{\circ}\)C, \(1950\,^{\circ}\)C, and \(2000\,^{\circ}\)C. During the experiment, both external and internal diameters were measured. Numerical studies of the movement and heat transfer during fiber drawing hereinafter were made in the Comsol Multiphysics 5.2 (finite element method for PDE) environment, the results for the inner diameter are shown in Figure 4. As can be seen from the figure, the numerical results obtained in the work coincided quite well with the experimental data. A similar picture was also observed for the outer diameter. Thereby, the modified model (5) proposed in the paper describes the process of drawing a quartz tube with a sufficiently high accuracy, considering all types of heat transfer occurring inside the furnace. Subsequently, for the convenience of generalizing the results obtained in the course of a numerical study of stability, the equations of the system were written in a dimensionless form. At the same time, it was assumed that  
\(z=\frac{\tau}{L}\), \(\tau=\frac{\tau_{0}}{L}\), \(V(z,\tau)=\frac{\
u(\frac{\pi}{\tau},\tau)}{v_{L}}\), \(R_{1}(z,\tau)=\frac{r_{1}(\frac{\pi}{\tau},\tau)}{L}\), \(R_{2}(z,\tau)=\frac{r_{2}(\frac{\pi}{\tau},\tau)}{L}\), \(T(z,\tau)=\frac{t(\pi,\tau)}{T_{a}}\), \(\lambda=\frac{\lambda_{ef}}{\lambda_{t}}\), \(\mu=\frac{\pi}{\mu_{0}}\), \(R_{f}=\frac{r_{f}}{L}\), \(T_{in}=\frac{t_{\mu}}{T_{a}}\), \(T_{out}=\frac{t_{\mu\mu}}{T_{a}}\), \(T_{f}(z,\tau)=\frac{t_{f}(\frac{\pi}{\tau})}{T_{a}}\). As a result, system (5) took the following form: \begin{table}
\begin{tabular}{c l l l} \hline \hline
**Symbols** & **Description** & **Symbols** & **Description** \\ \hline \(r_{f}\) & Furnace radius, [m] & \(\lambda_{ef}\) & Effective coefficient of thermal conductivity \\  & Preform surface emissivity coefficient & (molecular and radiative), [1] \\ \(\omega_{2}\) & outside of furnace, [1] & \(n_{c}\) & Refractive index of gas, [1] \\ \(\sigma_{0}\) & Stefan-Boltzmann constant, [1] & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 4: Dependence of the inner diameter of the capillary on the feed and draw speed, furnace temperature.
# Mind, Machine, and Meaning: Cognitive Ergonomics and Adaptive Interfaces in the Age of Industry 5.0
## 2 Human-Machine Interaction: Definitions and Key Concepts
### Factors Influencing Human-Machine Interactions
#### 2.3.3 Psychological Factors  
Psychological factors such as trust, stress, the emotional state, and user acceptance significantly shape human-machine interactions, particularly in the context of semi- and fully automated systems. Trust in automation is essential for effective HMI design; users must perceive the system as predictable and safe to engage with it confidently, especially in shared control environments like autonomous or rail vehicles [24]. Emotional and cognitive states--such as stress, mental fatigue, or emotional discomfort--can alter decision-making and situation awareness, potentially leading to errors or a loss of control [31]. Emerging technologies now enable real-time stress detection using physiological signals, including electrooculography (EOG) via smart glasses, allowing a dynamic adaptation of HMI elements to users' psychological status [32]. Emotional recognition through facial expression analysis is also gaining traction as a non-invasive way to assess workers' cognitive states and support ergonomic interventions [33].  
Furthermore, user acceptance remains a pivotal psychological construct that influences HMI success, shaped by perceived safety, ease of use, behavioral intention, and social influence--factors formalized in models such as TAM (Technology Acceptance Model), UTAUT (Unified Theory of Acceptance and Use of Technology), and AVAM (Automated Vehicle Acceptance Model) [34]. Poorly accepted HMIs may generate anxiety, hesitation, or a rejection of otherwise functional technologies, which is why standardized testing procedures and user-centered design are crucial in validating psychological alignment [35]. In addition, visual legibility, interface aesthetics, and message clarity influence users' perceptions, reducing mental workload and increasing comfort during interactions with automated systems [35].
## 5 Results and Discussion
### Turbulence Kinetic Energy in the Wake  
It is expected that the wake created by the disks augments the turbulence level with respect to the ambient value. It is now investigated how the computations of the added turbulence compare to the experimental results within the wake. Figure 7 shows the profiles of \(k\) (this is, \(k_{tot}=k_{scs}+k_{res}\) for the LES) at different downstream positions along the wake, when the inflow of the case Ti3 is used. There, it is observed that the LES results match quite well the measured (LDA data) turbulence levels behind both disks. However, it is noticed that, except for the nearest position to the disk, the LES predicts a higher diffusion of shear turbulence in the crosswise direction, an effect that is increased with the disk thrust.  
For the disks in the Ti12 case in Figure 8, the LES compare mostly well with the experimental data, although a small overestimation of \(k\) can be seen just behind the disk (\(x=2D\)). It is also observed that the shear layer originating at the edges of the disk is mixing faster with the ambient turbulence compared to the Ti3 inflow. Indeed, the effect of shear prevails deeper into the wake in the LES with the highest thrust disk, whereas it is mixed faster into the ambient turbulence when the thrust is lower. Results from the RANS computations are discussed in the next section.  
Figure 5: Vertical profiles of velocity deficit behind the disk \(C_{T}=0.42\) (**left**) and \(C_{T}=0.62\) (**right**), Ti3 case. RANS results in all figures are by [PERSON] et al. (2017).  
Figure 6: Vertical profiles of velocity deficit behind the disk \(C_{T}=0.45\) (**left**) and \(C_{T}=0.71\) (**right**), Ti12 case.  
It is also noticed that some inhomogeneities appear along the curves of LES, very noticeable in the simulations with the Ti12 inflow. These seem to indicate a footprint of the turbulence structures of the inflow turbulence. Although this feature could provide evidence of the need of performing averages in the azimuthal direction or creating synthetic turbulence that would cover longer simulation periods, it is thought that the results shown in the figures are sufficient for the purpose of these comparisons.
# Application of a High-Power Reversible Converter in a Hybrid Traction Power Supply System
## 3 Coordinated Control Strategy
### DC Output Characteristic of the Reversible Converter  
Typical DC output voltage characteristic of the reversible converter is constant. However, due to the demand of operating in parallel with the 12-pulse diode rectifier, it should have similar voltage droop characteristics.  
That means DC output voltage will drop with the increase of DC current, as follows:  
\[U_{\rm dc}=U_{\rm k}-R_{\rm dp}\times I_{\rm dc} \tag{5}\]  
where \(U_{\rm k}\) is the no-load output voltage for reversible converter. \(R_{\rm dp}\) is the equivalent resistance.  
A voltage droop control method based on output current feed-forward is presented in this paper, as shown in Figure 4. It includes three parts, which are called droop control, DC voltage control, and dq current control, respectively. Droop control is employed to calculate the target voltage \(U_{\rm dc}\)*, which decreases with the increase of the DC, complying with the law of Equation (5). The LIM1 will limit the value of the target DC voltage within the allowable range. DC voltage control adopts a classic proportional integral (PI) regulator without static error, so the influence of system parameter variation and external disturbance for the DC voltage can be eliminated, and the actual DC voltage can follow the target well. The output of the DC voltage control loop is the active current given value \(i_{\rm d}\)*, which will be limited by LIM2, in order to avoid the overload of the reversible converter. The dq current control is based on rotating synchronous coordinates d-q. Thanks to the coordinate transformation,and \(i_{\rm q}\) are DC components, and the PI compensator can reduce the error of the fundamental component to zero. With the proposed control method above, the linearity of the output curve can be guaranteed and strong system stability can be obtained.
# An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication
## 4 Results
### Analyzing the Classification Accuracy of the Proposed System Under Different Channel SNRs  
As explained in Section 3 under the methodology, since no image quality metrics are available for machine perception of a semantically transmitted image, a CNN model is used for image classification to emulate machine perception. Figure 3 shows how the CNN classification accuracy varies under different channel SNRs with various trained decoders. The simulation is conducted at the same SNR levels as those shown in Figure 8. At low SNRs (less than 2.5 dB), all trained decoders exhibit very low classification accuracy. At medium channel SNRs (3 dB to 3.5 dB), decoders trained with different channel errors achieve the best classification accuracy, while the decoder trained with zero error fails to perform effectively. At high SNRs (greater than 3.5 dB), all trained decoders achieve very high classification accuracy. Moreover, at high channel SNRs, all decoders perform similarly, regardless of their training conditions.  
Figure 8: SNR against accuracy (CNN for digit classification).