esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, XGBoost was effectively used to predict the relative density of pure magnesium components produced by Laser Powder Bed Fusion (LPBF). A significant finding was the identification of parameter interactions as the main factors accounting for 73.8% of the total feature importance, with laser power \(\times\) scan speed identified as the most influential interaction affecting component density. In terms of individual effects, higher laser power typically increased density, while scanning speeds that were too high decreased it--results consistent with established LPBF behaviour but quantified here for the first time for pure magnesium.  
The model showed that layer thickness had a small independent effect, although thinner layers tended to have higher densities. These findings confirm both established trends and the added value of explainable machine learning in uncovering subtle parameter interactions that influence part quality. The SHAP-based evaluation also provided clear visual representations that can contribute to informed process adjustments in future experiments or industrial applications.  
Despite these encouraging results, the prediction accuracy showed some variance at the edges of the parameter space, probably due to the sparse data distribution and possible process instabilities, such as balling or insufficient fusion. Extending the data set through further experimental trials--especially for less studied parameter combinations--will improve the reliability and versatility of the model.  
Future research should attempt to incorporate additional factors such as oxygen content in the build chamber, powder particle size distribution and post-processing procedures, which are known to influence LPBF results but were not addressed in this study. Furthermore, the inclusion of real-time monitoring systems (e.g., melt pool imaging and in situ thermal data) could improve the data richness and applicability of the models and ultimately enable a more reliable and scalable use of magnesium in additive manufacturing for structural or biomedical applications.  
K.S.: Conceptualization, Methodology, Software, Investigation, Data Curation, Formal Analysis, Writing--Original Draft, Visualisation, Validation. S.P.: Conceptualization, Methodology, Resources, Data Curation, Investigation, Validation, Visualisation, Writing--Original Draft. J.G.: Investigation, Data Curation, Writing--Review and Editing. M.F.: Writing--Review and Editing, Supervision, Validation, Funding Acquisition. I.D.: Writing--Review and Editing, Supervision, Validation, Project Administration, Funding Acquisition. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge the financial support from the Slovenian Research and Innovation Agency (research core funding No. P2-0157 and P2-0137 and research projects J7-60120 and J1-60015).  
The data will be made available on reasonable request.  
The authors declare that they have no known competing financial interests or personal relationships that could influence the work in this article.
# Infrared Image Deblurring via High-Order Total Variation and Lp-Pseudonorm Shrinkage
## 5 Experimental Results and Analysis
### Evaluation Metrics  
The main comparison evaluation metrics considered in this study are the PSNR, SSIM, and GMSD, which are expressed in Equations (19)-(21), respectively:  
\[\text{PSNR}=10\text{lg}_{-}10\frac{255^{2}}{\frac{1}{N}\sum\limits_{i=1}^{N} \sum\limits_{j=1}^{N}\left(\mathbf{X}_{ij}-\mathbf{Y}_{ij}\right)^{2}}, \tag{19}\]  
\[\text{SSIM}= \frac{\left(2u_{X}u_{Y}+255^{2}k_{1}^{2}\right)\left(2\sigma_{XY}+ 255^{2}k_{2}^{2}\right)}{\left(u_{X}^{2}+u_{Y}^{2}+255^{2}k_{1}^{2}\right)\left( \sigma_{X}^{2}+\sigma_{Y}^{2}+255^{2}k_{2}^{2}\right)}, \tag{20}\]  
where \(\mathbf{X}\) and \(\mathbf{Y}\) refer to the original image and the restored image, respectively. \(u_{X}\) and \(u_{Y}\) are the average of the sum of the images, and \(\sigma_{X}^{2}\), \(\sigma_{Y}^{2}\) represent the variance of \(\mathbf{X}\) and \(\mathbf{Y}\), respectively. Here, \(k_{1}\) and \(k_{2}\) are used to ensure that the SSIM expression is non-zero; in this experiment, \(k_{1}\) is set as 0.01 and \(k_{2}\) as 0.03.  
\[\text{GMSD}=\sqrt{\frac{1}{N}\sum\limits_{i}^{N}\left(\text{GMS}(i)-\text{GMSM }\right)^{2}}, \tag{21}\]  
where,  
\[\text{GMSM}=\frac{1}{N}\sum\limits_{i}^{N}\text{GMS}(i). \tag{22}\]  
\[\text{GMS}=\frac{2\mathbf{m}_{r}(i)\mathbf{m}_{d}(i)+c}{\mathbf{m}_{r}^{2}(i)+\mathbf{m}_{d}^ {2}(i)+c}. \tag{23}\]  
where \(\mathbf{m}_{r}\) and \(\mathbf{m}_{d}\) refer to the gradient amplitude of the image in the horizontal and vertical directions, respectively, and \(c\) is a constant of small value to guarantee that the denominator is a non-zero number.  
Figure 2: Test images. (**a**) Building.BMP; (**b**) Car.BMP; (**c**) Figure.BMP; (**d**) Man.BMP; (**e**) People.BMP; (**f**) Store.BMP; (**g**) Street.BMP; and (**h**) Woman.BMP.  
Larger PSNR values and smaller GMSD values correspond to better image recovery quality. The range of SSIM is \((0,1)\), where higher values indicate better deblurring performance. The iterative stop condition in the algorithm can be expressed as follows,  
\[E=\|\mathbf{F}^{(k+1)}-\mathbf{F}^{(k)}\|_{2}/\|\mathbf{F}^{(k)}\|_{2}\leq tol, \tag{24}\]  
where _tol_ is set to be \(10^{-4}\).
###### Abstract  
There are 24.5 million visually impaired and blind (VIB) students who have limited access to educational materials due to cost or availability. Although advancement in technology is prevalent, providing individualized learning using technology remains a challenge without the proper tools or experience. The TacPic system was developed as an online platform to create tactile educational materials (TEM) based on the image inputs of users who do not have prior experience in tactile photo development or 3D printing. The TacPic system allows the users to simply upload images to a website and uses AI cloud computing on the Amazon Web Services platform. First, it segments and labels the images. Then, the text label is converted into braille words. Subsequently, surface rendering and consolidation of the image and text is performed, before it is converted into a single file that is ready for 3D printing. Currently, the types of TEM that can be created are tactile flashcards, tactile maps, and tactile peg puzzles, which can be developed within a few hours. This is in contrast to a development period of weeks using traditional methods. Furthermore, the tactile educational materials were tested by two VIB teachers and six VIB students. It was found that those who are congentially blind need more time to identify the object and rely more on the braille labels compared to students who became blind at a later age. Teachers also suggested producing TEM that use simpler images, and TEM that are suitable for both sighted and VIB students. In conclusion, the researchers successfully developed a platform that allows more educators or parents to develop personalized and individualized TEM. In the future, further optimization of the algorithms to improve segmentation and the inclusion of other features, such as color, could be undertaken. Finally, new printing materials and methods are needed to improve printing efficiency.  
visually impaired and blind (VIB); tactile educational materials (TEM); AI cloud computing; 3D printing +
Footnote †: journal: Applied Sciences  
Accepted: 16 August 2021
###### Abstract  
Anterior cruciate ligament (ACL) injuries pose significant challenges, driving the need for innovative repair strategies. Tissue engineering (TE) has emerged as a promising field for ACL injuries. Wet spinning is a filament production technique that enables precise control over filament alignment, diameter, and porosity, making it suitable for developing new scaffolds for ACL injuries. This study develops fibrous scaffolds using wet spinning of polycaprolactone (PCL) reinforced with cellulose nanocrystals (CNC) to enhance mechanical properties. Wet spinning was employed to fabricate fibrous scaffolds, utilizing PCL as the primary polymer due to its favorable biocompatibility and degradability. An automated collector was developed and optimized, which allowed the stretching of the PCL filaments to diameters as low as 30 \(\upmu\)m. Several filaments were explored and characterized using SEM, TGA, and mechanical tests. The optimized PCL/CNC filaments were used to develop 3D braided structures that mimic ACL structure. The combination of wet-spun PCL/CNC filaments (with an optimized stirring method) and the braiding procedure allowed for fully biocompatible scaffolds that mimic both the structure and the mechanical properties of native ACL. Cytotoxicity and biocompatibility tests showed cell viability and proliferation values above 99% and 81%, respectively. These findings underscore the potential of CNC-reinforced PCL scaffolds as promising candidates for ACL repair, laying the groundwork for future biomedical applications.  
anterior cruciate ligament; polycaprolactone; cellulose nanocrystals; wet spinning; tissue engineering 1
## 3 Results and Discussion  
### Fruit Firmness  
Firmness is an important parameter that is associated with the ripeness, harvest time and quality grade of fruit [22]. The results of the measurements of the firmness parameter for the control and magnetic field-treated fruit of the Idared and Ligol varieties are shown in Figures 3 and 4, respectively. Fruit firmness in both groups decreased continuously over the duration of cold storage and after the 14 days in room conditions for each experiment. After 14 days in room conditions, fruit firmness was higher in the apples that were treated with the magnetic field compared to the control in all cases that were tested. It could be seen that using a magnetic field of 100 uT with a frequency of 50 Hz for both of the studied varieties produced an observed increase in the firmness parameter of up to 10%, depending on the characteristics of the fruit. There were significant differences between the control fruit and those treated with a magnetic field. These could have been influenced by the metabolism of the fruit, which is responsible for the rapid conversion of protopectin into pectin and is related to fruit firmness [23]. The higher firmness values that were found in this study after magnetic field treatment were also likely due to the electroporation that was induced by the electrical impulses, which increases the permeability of cell membranes [24]. In this way, the cell membranes can recover their integrity and structure, which results in firmer tissues. These results showed that the MF treatment retarded the softening of the fruit tissue. Therefore, the application of MF treatment at 100 uT for 8 h per day is the proposed method for maintaining the firmness of apples. The same observations were confirmed by the study of [PERSON] et al. [25]. Their results also showed an increase in the postharvest firmness of strawberries of up to 30% using an AMF of 50-150 uT in comparison to a control. MFs penetrate deeper into tissues, which may cause changes in cellular metabolic pathways [11]. Another team of scientists analysed the effects of an electric field on tomatoes and apples. They reported that the electric field permeabilised the cell membrane, which resulted in tissue softening. The pulsating electric field reduced the loss of turgor and the fracture of apple cell membranes [26] and the firmness value of tomatoes [27].  
Figure 3: Effects of MF on the firmness of Idared apples after 120 days of cold storage. For each experiment, apples were stored in room conditions for 14 days. Statistically significant differences are marked by different letters. Differences between average values that are marked with the same letters (a,b) are not statistically significant. The data were analysed separately for each experiment.
## 2 Current Status of Development of Geophysical Exploration Techniques
### Other Types of Exploration Techniques
#### 2.4.2 Temperature-Field Inverse Analysis Method  
The heat conduction equation for earth and rock dams [73] is typically established in a coordinate system with the center of the seepage channel as the origin. When seepage occurs in earth and rock dams, heat exchange occurs between the fluid in the seepage channel and the soil body, resulting in changes in the temperature of the stratum near the seepage channel. The detection is made in the measurement coordinate system to determine the location of the seepage channel. The concept of a temperature field was proposed by foreign scholars in the 1960s. The temperature-field inverse analysis method is an inversion method based on the temperature field, which is based on the premise that there are a large number of boreholes through which the leakage channels pass, and the temperature field is used to detect and analyze the hidden dangers in earth and rock dams [74]. [PERSON] et al. [75] established a temperature control feedback mechanism based on an inverse analysis of the actual measured data in the field, producing better results in actual projects. To quantitatively analyze the exact location and size of leakage channels, [PERSON] et al. [76] established a temperature-field leakage detection model for several concentrated leakage channels and performed inversion of the measurement line optimization of the leakage channels to analyze the feasibility. The temperature-field inverse analysis method is typically used without considering the complex thermal boundary problem and the influence of natural ground temperature. The results would have been more accurate if the original ground temperature distribution had been available.
# Study on the Effect Mechanism of MRD to Rotor System
###### Abstract  
Magnetorheological damper (MRD), a semi-active control device, has the stability of the passive control device and the adaptive ability of the active control device. It is considered to be an ideal replacement for the squeeze film damper (SFD) and has been extensively investigated. However, the effect mechanism of MRD to a rotor system is still unclear, which will influence the structural design and control strategy of MRD. This paper aims to propose a general MRD excitation model for a rotor system and discover the effect mechanism. First, the effects of current and eccentricity on mechanical properties of MRD are studied in detail and approaches suppressing the vibration amplitude of a rotor system are put forward. Then, a general model considering MRD, ball bearing and squirrel cage is proposed. Based on the finite element method, the dynamic equations of a rotor-bearing-squirrel cage system with MRD and mass imbalance are presented. Finally, the influences of MRD on the dynamic characteristics of the rotor system are investigated, and the effect mechanism is discovered. In addition, the nonlinearity which is not desirable as it causes fluctuating stresses in the main components of the rotor system and eventually leads to their failure due to fatigue, are observed. The changes of damping and stiffness of MRD can alter the damping performance of MRD and the suitable range of current forms the zone of effective damping, which lays helpful guidance for the design and application of the MRD.  
MRD; semi-active control; rotor system; dynamic behavior Article
# Nanoencapsulation of Essential Oils as Natural Food Antimicrobial Agents: An Overview
## 5 Main Strategies for Nanoencapsulation of Essential Oils
### Equipment-Based Nanoencapsulation  
In order to encapsulate bioactive compounds, it is usually necessary to use some general equipment, including homogenizers, grinders, mixing equipment, and so on. However, certain specific requirements, such as nanofibers and nanofibrous scaffolds, can be implemented only by specialized developed equipment such as electrospinning and electrospray (Figure 4) [126]. Electrospinning and electrospray are two types of electrohydrodynamic procedures that use charged jets to rotate or spray a polymer solution to produce fibers or particles. The prospect of using both technologies in food preservation is promising due to the following advantages: amendable size with large surface area, ability to carry heat-sensitive compounds, and possibility of mass production. Electrospinning technology has been used to prepare antimicrobial nanofibers (Figure 2F) to encapsulate bacteriocin in probiotics for sustained release during food processing and storage [127]. [PERSON] and [PERSON] [128] successfully encapsulated peppermint essential oil into alginate biopolymer using the electro-spraying method. Their results showed that the peppermint EOs did not degrade during the encapsulation process and showed a high encapsulation efficiency (96.4%).
## 1 Introduction
Nevertheless, none of the systems mentioned above have simultaneously addressed the need for a MCC-based system aiming specifically to support the KM process in SME construction companies. For example, a MC-based BIM system such as BIM360 uses CC and was designed for the construction industry, but it was not designed to support SME's KM process. Alternatively, mobile and cloud KM systems or collaborative systems, such as XWiki or Slack, were designed to support communication and knowledge sharing among team members, but were not designed to support domain specific issues of KM processes in the construction industry. Therefore, considering the above context, this work focuses on the following research question: \"How can a user-centered designed MCC KM platform be developed that is user-friendly, inexpensive, and capable of improving the KM process in SMEs of the Chilean construction industry?\"  
This research develops a Lessons Learned (LL) platform as a tool to manage knowledge to answer the above question. This article focuses on a user-centered design to develop this platform. To emphasize this approach, the lean development methodology that allows for iterative product development was adopted. This methodology comprises semi-structured interviews that were conducted in three Chilean construction companies, and the design, implementation, and evaluation of the MCC-based platform to support the KM process. This research encompasses three main contributions. First, it presents a KM platform that was specifically designed to support the KM process required to improve the on-site execution of construction projects in construction SMEs, promoting a structured dissemination of the knowledge generated in each of the projects developed by the company. For this, the specific characteristics and needs of construction SMEs were taken into consideration, allowing the alignment between the nature of the knowledge generated in these companies, and their operative and technological reality. The use of mobile and collaborative technologies, on a mobile cloud computing environment, makes the KM platform more economical, easy to use, and easy to adopt by construction SMEs. Second, it illustrates how to develop a KM platform using a MCC environment, iteratively and incorporating users. In addition, finally, it describes the validation of the system with real potential users, who used the tool in real environments. The rest of this article is organized into the following sections. Section 2 focuses on KM and how it has been used in SMEs. Section 3 discusses the requirements of the KM process in construction SMEs based on the literature. We then explain our research methodology based on the lean development process in Section 4. Section 5 introduces the architecture of the KM MCC platform. Next, we present the results of the evaluation of the platform that was made to the end-users and construction experts in Section 6. Finally, we conclude with a summary of the research results and the contributions to the body of knowledge, and make suggestions for future research, in Section 7.
# Detection of Movement and Lead-Popping Artifacts in Polysomnography EEG Data
## 4 Comparison with the Expert-Assessed Ground Truth  
The expert-assessed ground truth was based on the manual annotation of the C3 channel of the PSG EEG by an AASM-certified sleep physician. The PSG EEG had 1848 epochs. Some of the NREM and REM artifact epochs identified in the preprocessing step were excluded from the comparison. As a result, the comparison involved 46,422 segments (1562 of the 1848 total epochs) during NREM and REM sleep. We compared expert-annotated artifact regions with those identified by the algorithm by counting segments that the two classifications had in common.  
The performance of the algorithm as measured by kappa and accuracy was largely insensitive to the choices of the two thresholds (Table 1). For the default choices, the overall accuracy was around 88%, with sensitivity and specificity of 80% and 91%, respectively. Systematic performance comparisons for additional local and global thresholds are presented in Tables A3 and A4.  
Figure 4: An illustration comparing expert-annotated artifact regions and algorithm-identified artifact regions in channel C3 for a single subject. Top panel: timing of NREM and REM sleep cycles. Second panel from top: spectrogram with frequency (Hz) on the Y axis, time on the X axis, and EEG power color-coded with cool to warm representing low to high power. Third panel from top: algorithm-identified artifact regions marked as black bands. Bottom panel: expert-annotated artifact regions marked as black bands.