esa-sceva/satcom-chunk-collection huggingface dataset:
# Wavelength-Dependent Nonlinear Absorption in Palladium Nanoparticles
## 4 Conclusions  
The nonlinear absorption of PdNPs was studied by the OA Z-scan method using 500 nm, 550 nm, 600 nm and 650 nm nanosecond pulsed lasers. The results show that, although the SPR wavelength of PdNPs is far away from the excitation wavelength, the SA and the transition from SA to RSA can still occur. Moreover, the nonlinear absorption is wavelength- and energy-dependent. This research implies that PdNPs can be used to develop broadband saturable absorbers and optical limiters.  
Methodology and writing--original draft preparation, C.C.; investigation, Y.G.; data curation, J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Provincial Natural Science Foundation: F2018027.  
Not applicable.  
The experimental data were measured in the Femtosecond Laser Laboratory of Heilongjiang University.  
The authors declare no conflict of interest.  
Figure 5: The characteristic curve of wavelength-dependent nonlinear absorption. Double \(Y\)-axis characteristic curves are shown in (**a**), where colored dotted lines represent the saturation strength \(I_{s}\), and for comparison, the black line represents linear absorption spectrum. Double \(Y\)-axis characteristic curves are shown in (**b**), where colored dotted lines represent the nonlinear absorption coefficient \(\beta\), and for comparison, the black line represents linear absorption spectrum.
## 2 Proposed Speech Enhancement Methods for LDVs  
We first discuss the problems with LDVs when acquiring sounds then introduce the two proposed speech-enhancement methods, i.e., STFT- and waveform-based methods. The STFT-based method involves STFT-based two-stage processing of amplitude and phase reconstructions, and the waveform-based method involves waveform-based two-stage noise suppression processing and high-frequency component reconstruction. Each method is described in the following sections. All variables are listed in Table 1.
## 6 Conclusions  
This study was conducted with the objectives of maintaining quality accuracy of the panel block on the assembly stage and providing a detail plan of temporary piece installation during joint welding of the panel block. The major results obtained are as follows:  
1. The butt-welding experiments carried out to determine the temporary piece installation number impact on welding distortions demonstrated that the angular distortion and longitudinal bending deformation decreased when the piece installation number increased.
2. The deformation calculation method for butt joint welding with temporary pieces attached was developed using the thermal elasto-plastic finite element analysis. The comparison between the experimental and analysis results confirmed that the proposed analysis method is sufficiently suitable to simulate the butt-welding deformation considering the installation of pieces.
3. In order to apply the proposed method to large scale blocks with temporary pieces installed, welding deformations of the panel block were measured, and butt-welding deformation analysis was performed by generating the finite element model of the same panel block. The measurement and analysis results show good agreement in both qualitative and quantitative terms.
4. By calculating the proper number of temporary pieces installed, the possibility of reducing the welding time and number of pieces currently used was examined by welding deformation analysis. The results indicate that, for large panel blocks, the temporary piece usage can be theoretically reduced by up to 46%. However, in a shipyard setting a lower reduction percentage is expected, because the calculations do not reflect unexpected field conditions, such as the initial deformation of the weld joint line in the piece assembly or the effect of the pieces installed to adjust the misalignment.
5. The proposed analysis method is expected to be useful in a variety of fields as an analysis tool for planning the salvage of temporary pieces, through the preparation of piece deployment drawings and installation guides at a preliminary stage.  
Conceptualization, H.R. and K.L.; methodology, H.R. and K.L.; software, H.R. and S.K.; validation, H.S. and S.K.; formal analysis, H.R. and S.K.; investigation, H.R. and K.L.; resources, H.R. and K.L.; data curation, S.K.; writing--original draft preparation, H.R and K.L.; writing--review and editing, S.K. and K.L.; visualization, S.K.; supervision, H.R.; project administration, H.R.; funding acquisition, H.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 4 Numerical Simulation and Discussion
### Governing Equations and Constitutive Models
#### 4.1.3 Equilibrium Equation  
The deformation process of a saturated porous medium is assumed to be isotropic linear elastic. Considering the contribution of the pore-pressure with the principle of effective stress and thermal stress, the local equilibrium equation in terms of Lame constant can be written as  
\[(\lambda+\mu)U_{j,ii}+\mu U_{i,jj}+F_{i}+(\alpha p)_{,i}-(3\lambda+2\mu)\beta T _{,i}=0, \tag{5}\]  
where \(\lambda\) and \(\mu\) are Lame constants, \(F\) is the body force, \(\alpha\) is Biot-Willis coefficient, \(\beta\) is the thermal expansion coefficient, and \(U_{j}\) is the displacement component [40].  
Equation (5) describes an equilibrium state which neglects the inertial effects. Its application to the case of time-dependent flow and heat transfer model is valid because the time scale of the structural response is generally many orders of magnitude faster than the time scale of the flow and heat transfer. Thus, even the change in stress and strain with time in equation appears stationary, and the structure-to-fluid coupling term, which involves the rate of strain, is nonzero [40].
# Lacase in Biorefinery of Lignocellulosic Biomass
## 2 Pretreatment of Lignocellulosic Biomasses
### Hydrothermal Pretreatment for Lignocellulosic Biomasses  
Hydrothermal pretreatment is a process that involves the treatment of lignocellulosic biomasses with hot pressurized water or steam. The process typically takes place at high temperatures, mostly in the range between 160 and 260 \({}^{\circ}\)C, and pressures between 5 and 30 bar [20]. There are several types of hydrothermal pretreatments, including steam explosion, and liquid hot water [21]. Steam explosion is a process that involves the rapid depressurization of hot pressurized water, which causes the explosion of the biomass fibers and the release of soluble sugars [22]. Liquid hot water pretreatment is a process that involves the treatment of biomass with hot pressurized water, without any explosive decompression [23]. The high temperature and pressure conditions cause the breakdown of hemicellulose, which is the most labile component of lignocellulosic biomasses. The breakdown of hemicellulose results in the formation of soluble sugars, which can be easily converted into value-added products.  
The process also leads to the partial depolymerization of lignin, which makes the remaining cellulose more accessible to enzymes for hydrolysis. The advantages of hydrothermal pretreatment include the removal of lignin, thereby making the biomass more amorphous, leading to an enhancement of enzymatic hydrolysis. The disadvantages include the generation of inhibitory compounds, such as acetic acid and formic acid, whichcan inhibit downstream enzymatic hydrolysis [20]. Hydrothermal processes, coupled with solid catalysts, have been explored for biomass valorization [24].
## 4 Results  
### Descriptive Statistics and Linear Regression Estimation  
Table 3 presents descriptive statistics for the items included in the study. Among generative AI (GAI) tools, ChatGPT in any of its versions is the most widely used. The average value of the variable aggregating usage frequency across all tools (USEINT) is 1.87, which, according to Table 2, corresponds to usage between \"once\" and \"several times a year\". Notably, the items related to performance expectancy (PE) showed high evaluations, with values ranging from 6.28 to 7.88 on a 10-point scale. In contrast, self-reported knowledge (KNOW) had a mean score of only 4.26 out of 10. The average score for items capturing perceived societal benefits (SU) did not reach a neutral value of 2 on a three-point scale. Conversely, the items addressing perceived societal risk (SR) range from 3.57 (SR4) to 4.37 (SR1), exceeding the neutral value of 3, indicating a generalized perception of risk. Finally, the items with perceived need for regulation (REG) scores above 4 reflect a predominantly favorable opinion toward the regulation of AI development and use. All factor extractions for the multi-item variables (PE, INNOV, PRI, SU, SR, and REG) exceed the 0.6 loading threshold, and the first principal component in each case explained, on average, more than 50% of the variance, supporting the reliability of the measurement constructs. Using the variable values defined in Table 2, Spearman correlations with the USE were computed, and all were statistically significant (\(p<0.001\)). These values are shown in Figure 2. As expected, PE, INNOV, KNOW, SU, ED, and IN showed positive correlations with GAI use, whereas perceived societal risks (SRs) and the need for regulation (REG) were negatively correlated. Additionally, the negative correlations between SEX and GEN suggest that older generations and women are less inclined to use GAIs. Interestingly, the correlation for privacy risk perception (PRI), although weakest, is positive. Thus, the Spearman correlations support all the hypotheses proposed in Section 2, except H3, which predicts a negative correlation between PRI and USE. Figure 2: Spearman correlations of explanatory variables with USE. Note: All correlations are significant for \(p<0.001\). Regarding the linear regression estimation, whose results are shown in Table 4, the results closely align with the Spearman correlations, with the notable exception that societal risk (SR) is not statistically significant. The model fit is acceptable, with an R\({}^{2}\) of 39.12%, indicating moderate explanatory power. The regression analysis shows that KNOW is by far the most influential predictor of usage (\(\beta\) = 0.709, \(\eta^{2}\) = 0.2494, \(p\) \(<\) 0.001), underscoring its central role in GAI adoption. Other relevant positive predictors include INNOV (\(\beta\) = 0.228),
# Automation of Experimental Modal Analysis Using Bayesian Optimization
## 2 Proposed Method
### Hyperparameter Optimization
To measure the match between two FRFs, the \(\mathtt{FRAC}\) can be used:  
\[\mathtt{FRAC}(\mathbf{H}_{1}(\omega),\mathbf{H}_{2}(\omega))=\frac{|\mathbf{H}_{1}(\omega )^{T}\mathbf{H}_{2}^{*}(\omega)|^{2}}{\big{(}\mathbf{H}_{1}(\omega)^{T}\mathbf{H}_{1}^{*}( \omega)\big{)}\big{(}\mathbf{H}_{2}(\omega)^{T}\mathbf{H}_{2}^{*}(\omega)\big{)}}, \tag{1}\]  
where \(\mathbf{H}_{1}(\omega)\) and \(\mathbf{H}_{2}(\omega)\) are the FRFs being compared, \(\omega\) is the angular frequency, and \(\mathbf{x}^{*}\) is the complex conjugation and \(\mathbf{x}^{T}\) the transpose of a vector \(\mathbf{x}\)[19]. \(\mathtt{FRAC}\) values of 100% indicate a perfect match, whereas values of 0% mean no correlation at all. An average value over all input FRFs is computed to assess the complete input data set:  
\[\overline{\mathtt{FRAC}}=\frac{1}{n_{inp}}\sum_{k=1}^{n_{inp}}\mathtt{FRAC}( \mathbf{H}_{inp,k},\mathbf{H}_{model,k}) \tag{2}\]  
In this case, \(n_{inp}\) is the number of input FRFs, \(\mathbf{H}_{inp,k}\) are the original input FRFs, and \(\mathbf{H}_{model,k}\) are the FRFs resulting from the modal model. Note that the angular frequency \(\omega\) has been dropped for the sake of readability. The concordance between the model FRFs and the input FRFs generally increases with an increasing number of modes in the model. To prevent spurious modes approximating noise in the measurement data, Equation (2) is extended by a (small) regularization parameter \(r\), which penalizes a high number of found modes \(n_{modes}\):  
\[y=\overline{\mathtt{FRAC}}-n_{modes}r \tag{3}\]  
The final optimization cost function \(y\) can then be constructed by combining Equations (1)-(3), with \(\mathbf{H}_{model,k}\) in Equation (2) being a function of the hyperparameters to be optimized:  
\[\mathbf{H}_{model,k}=f(k,p_{max},k_{\Delta f},k_{\Delta D},k_{Dmin},k_{Dmax},d_{ max},q_{min},k_{eul}) \tag{4}\]  
As shown in Figure 1, the Bayesian optimization is terminated when the current iteration number \(i\) exceeds the set maximum iteration number \(n_{max}\).
# Electrical and Thermal Behaviour of Crystalline Photovoltaic Solar Modules in Shading Conditions
###### Abstract  
The shadow effect caused by nearby objects or the lack of cleaning significantly affects the performance of photovoltaics (PV) installations. This article analyses the bypass diode electrical behaviour and the thermal response of a PV crystalline module under shading or soiling conditions. PV cells of different substrings were covered progressively to simulate the effect of shading or soiling while a programmable electronic DC load was connected to a PV module to set an operating voltage. Three different tests were made to different PV crystalline technology. The paper characterizes in real conditions the I-V curve, bypass diode current, and front and back side PV cell temperature with contact sensor and infrared (IR) thermography, respectively. The results showed that the operation voltage established in the PV module defines the electrical bypass diode current and thermal response under normal operating conditions, shading or soiling. To show the bypass diode behaviour in such conditions, I-V curves were obtained, pointing out the value of the current that flows through bypass diodes in the whole voltage range.  
PV system performance; bypass diode; shading; soiling; IR thermography Article
###### Abstract
Additionally, the monitoring of 3D printing operations can be facilitated through web-based applications linked to an Internet of Things (IoT) framework. These applications collect data from multiple sensors affixed to the printing device, allowing for the real-time assessment and optimization of printing processes [27]. The field of 3D printing has seen a rise in specialized sensors that are revolutionizing the way the process is monitored and controlled. [PERSON] [28] and [PERSON] [29] have both contributed to this trend, with [PERSON] focusing on sensor arrays that track motion/vibration, temperature, orientation, and humidity, while [PERSON] has developed 3D-printed optical sensors specifically designed for metrology purposes. Additionally, [PERSON] [30] has introduced a flexible strain sensor with intricate microchannel features, showcasing the versatility of 3D printing in creating complex sensor designs. These advancements are poised to significantly improve the quality and efficiency of 3D printing technology. Building on the existing research, this study aimed to optimize critical FDM printing parameters--specifically the layer height, temperature, and speed--using precise instrumentation and detailed perimeter accuracy analysis through box plot statistics. Advanced measurement tools, including a Keyence laser scanner and microscope, were employed to assess the dimensional accuracy and surface quality, focusing on enhancing the consistency of the outer perimeters of printed objects. This research seeks to comprehensively understand how the outer-perimeter and surface quality variables impact the overall print quality, with the findings contributing to the refinement of FDM techniques and insights for improving the print accuracy in industrial settings.
# Low-Frequency Expansion Approach for Seismic Data Based on Compressed Sensing in Low SNR
## 3 Low Frequency Expansion
### Algorithm Implementation  
In the framework of CS theory, low-frequency information can be recovered in the frequency domain by using the sparsity of reflection coefficients in the time domain and frequency domain. This method is greatly affected by noise; that is, the reflection coefficient is more accurate when noise is weak. Otherwise, the accuracy of reflection coefficient will be greatly reduced. In this section, well data are introduced into the cost function to constrain the inversion process.  
The implementation process of the algorithm after introducing the well data is shown in Figure 3.  
Formula (13) is solved by gradient descent algorithm, and the specific implementation algorithm is as follows:  
Initializing: \(\forall\mathbf{r}_{0}\), \(L_{in}\) (maximum number of iterations in the inner loop), \(L_{out}\) (maximum number of iterations in the outer loop), \(\varepsilon\) (residual error), \(k=1\);  
Well data processing: the reflection coefficient \(\hat{\mathbf{r}}_{w}\) is estimated by well data;  
Iteration start:  
\(\mathbf{r}^{0}=\mathbf{r}_{0}\);  
External circulation: judging \(\|\mathbf{G}-\mathbf{W}\mathbf{F}\mathbf{r}^{k}\|_{2}>\varepsilon\)_and_\(k\leq L_{out}\)  
\(\mathbf{r}^{0}=\mathbf{r}^{k-1}\)  
Internal circulation: \(j=0\) to \(L_{in}\)-1, \(\mathbf{r}^{j+1}=T_{\lambda_{k}}(\mathbf{r}^
## 2 Structure  
The transition from ESIW to a rectangular waveguide is relatively simple from the electromagnetic point of view, since the ESIW itself is a rectangular waveguide of reduced height. Therefore, the main challenge in the transition proposed is not the matching of the original propagation mode to that of the new structure (which in this case coincides), but to ensure that the transition fully allows its propagation and transmission to take place with the lowest possible insertion and reflection losses. The coupling between the two rectangular waveguides --the planar waveguide (ESIW) and the 3D waveguide--will be improved as the electrical contact in the long walls of both waveguides is optimised, since in those walls the current changes direction constantly. Conversely, a lack of continuity in the short walls would not impact the transmission of the fundamental mode because the current along those walls is orthogonal to the propagation direction, making the connection between those short walls less critical. On the other hand, it is essential to ensure precise alignment of the waveguides, as small misalignments can result in unwanted reflections. To summarise, the proposed 3D structure must ensure accurate alignment of the waveguides when assembled with the planar circuitry, and provide physical access to the long walls of both waveguides to allow tin soldering in order to ensure a very good electrical continuity. As previously indicated [12], systems such as ESIW and waveguides belong to the same category of transmission systems, obviating the requirement for signal adaptation between them and phase discontinuities. Hence, with assured electrical continuity, the transitional performance is expected to be satisfactory, as we can see in the simulation shown in Figure 1. Furthermore, the welding must exhibit sufficient strength to forestall detachment of the device from the remainder of the structure. In this study, the primary focus has been on ensuring the feasibility of the transition in its simplest form. For future work, possibilitiesfor weight reduction, volume minimisation, design simplification, and other aspects may be explored. These objectives extend beyond the scope of this prototype and are going to be explored once this prototype has been validated.
# Research on a Real-Time Control System for Discrete Factories Based on Digital Twin Technology
###### Abstract  
Gear factories are most typical discrete manufacturing factories. Many gear factories are striving to explore how to achieve intelligent manufacturing in order to improve efficiency and reduce costs. Digital twin technology is currently one of the most reliable ways to achieve intelligent manufacturing. This article aims to establish a real-time control system in order to promote intelligent manufacturing for discrete manufacturing factories. Firstly, a model for a digital twin gear factory is put forward based on the characteristics of gear factories, and the composition of a real-time control system for gear factories is clarified. Then, a human-computer interaction architecture for the real-time control system is proposed. The real-time control system consists of three parts as follows: a monitoring module, a virtualizing module, and a controlling module. At work, it appears as a kind of human-machine interaction form with the three following interfaces: a monitoring window, a virtualizing window, and a controlling window. Finally, a gear factory, which is specialized in manufacturing the intermediate shaft dual gear of a new energy vehicle gearbox, develops a set of software for the real-time control system. The prototype software is obtained through some development activities such as 3D MAX and WebGL virtualization modeling and OPC UA and REST communication design.  
discrete manufacturing; digital twin factory; real-time control system; dual gear factory; software development; virtualization technology; communication standard +
Footnote †: journal: Applied sciences
## 6 System Application Verification
#### 6.1.2 Program Editing and Track Making  
(1) PLC program editing: Using TIA PortalV16 software, modular programming and ladder diagram logic are employed to create and edit the PLC program, resulting in execution commands for the motion mechanism. To start, launch a new TIA Portal project, set up the production line equipment, define PLC variables, and then edit control instructions according to motion timing. Furthermore, complete PLC program writing, HMI editing, compilation, and debugging to ensure the real-time transmission of physical production line data to the digital twin virtual production line to achieve synchronization and visualization.  
(2) Industrial robot program editing and trajectory production: During task execution, industrial robots must detect real-time environmental data, including the locations of  
Figure 11: Schematic diagram of automobile wheel hub intelligent manufacturing production line based on digital twin.  
workpieces, obstacles, and their own positions. In unstructured settings, achieving precise control over the robot's trajectory, speed, and force is crucial. In the PQ Factory software, the robots' motion trajectory is planned in detail according to the process requirements. The end-effector path, speed, and direction are designed, and the transition points are set to avoid collisions and strange attitudes. Once the trajectory is finalized, interference verification is performed, followed by setting up the simulation mode to ensure safe and efficient operation. Figure 12 illustrates the process of robot program editing and track creation, encompassing trajectory optimization and the use of control signals for precise navigation.