esa-sceva/satcom-chunk-collection huggingface dataset:
# Encapsulation of Fennel Essential Oil in Calcium Alginate Microbeads via Electrostatic Extrusion
## 2 Materials and Methods
### Characterization of Fennel EO Microbeads
#### 2.5.5 Gas Chromatography-Mass Spectrometry Analysis  
To assess the composition of fennel EO before and after encapsulation, GC-MS analysis was conducted following the method outlined by [PERSON] et al. [1]. This analysis utilized an Agilent Technologies 6890N Network GC System with an Agilent 5973 inert mass selective detector and a capillary column (Agilent HP-5 MS: 5%-phenyl-methylpolysiloxane; dimensions: 30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m). Samples were diluted (1:99) in a mixture of \(n\)-hexane and an internal standard (nerol, concentration: 1.0518 mg/mL), and 1.0 \(\upmu\)L was injected automatically by Agilent 7683B autosampler injector in split mode at a 1:100 ratio, with an injection temperature of 250 \({}^{\circ}\)C. Helium served as the carrier gas, flowing at a constant rate of 1 mL/min. The oven temperature program ranged from 60-145 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C/min, followed by 145-250 \({}^{\circ}\)C at a rate of 30 \({}^{\circ}\)C/min, maintained at the final temperature for 3 min. The transfer line, MS source, and quadrupole temperatures were maintained at 280, 230, and 150 \({}^{\circ}\)C, respectively. The ionization energy of the detector was set at 70 eV. Mass spectra (\(m/z\)) were recorded within the range of 30-550 at a scan rate of 1 scan/s, with quantification performed in single ion monitoring (SIM) mode. These conditions were consistent for analyzing the alkane solution, and retention indices (RI) were computed as per [PERSON] et al. [19]. Identification of EO components relied on comparing their retention times, RI, and \(m/z\) values with authentic standards and entries in the NIST library (Chem Station Data Analysis). Volatile compounds were quantified using calibration curves of \(\alpha\)-pinene, \(\beta\)-pinene, camphene, myrcene, \(\alpha\)-phellandrene, \(p\)-cyrene, \(\alpha\)-terpinene, \(\gamma\)-terpinene, D-limonene, eucalypt, L-fenchone, estragole, camphor, \(p\)-anisaldehyde, carvone, and trans-anethole. Sabinene and cis-sabinene hydrate were identified based on their \(m/z\) values, RI, and comparison with the literature data, while the calibration curve for 3-carene was utilized for their quantification. The concentration of the quantified compounds was expressed as mg/mL of EO.
## 2 Design and Fabrication  
This study examined the Cu and Sn intermetallic compound formation as a result of electromigration from current stress and heat. Figure 1 shows the sample production process. The samples were made of Ta/Cu/Sn thin films with a thickness of 250 nm, 20 \(\upmu\)m, and 20 \(\upmu\)m, respectively, deposited on the silicon substrate as an adhesion layer using high-power impulse magnetron sputtering (HiPIMS). Initially, HiPIMS was used to sputter a Ta film (250 nm thick) as the diffusion barrier layer. Next, DC power was used to sputter a Cu film (550 nm) as the seed layer. After, as shown in Figure 1b, the sample was placed in the plating solution and a Cu film (20 \(\upmu\)m) was electroplated (detailed in Table 1). As presented in Figure 1c, 10 vol.% sulfuric acid was used to remove the surface oxides on the copper film surface, and then deionized water was used to remove the chemical residues attached to the surface; next, the sample was spin-coated with AZ P4620 positive photoresist, and patterning was done after. Finally, in Figure 1d, the sample was electroplated with a Sn film (20 \(\upmu\)m) (detailed in Table 1) and photoresist was lifted off. Figure 1e presents the completed sample. After deposition, the sample was cut into three different shapes as shown in Figure 2.  
This study deposited a Ta film to prevent Cu atoms from diffusing into the Si substrate. The diffusion activation energy of Cu and Sn is only 0.43 eV; therefore, Cu atoms can easily diffuse into the Si substrate. Previously, tantalum was found to be used as an excellent diffusion barrier between Cu and silicon substrate [19]; its atoms cannot easily diffuse to other materials, so it was deposited between Cu and Si to inhibit the diffusion of Cu atoms into the Si substrate.
# Roadmapping of Nanoelectronics for the New Electronics Industry
## 5 SoC and SiP  
In the past 15 years, the electronics industry has substantially changed due to the pervasive deployment of mobile phones, Wi-Fi stations, together with the success of joint work between foundries and fabless companies. Today, the conception, design, and realization of any ICs by system integrators do not need integrated device manufacturers, including the integration of multiple functionalities in a single chip (System on Chip/SoC) or multiple dice in a single package (System in Package/SiP). These strategies of integration allow to reduce the cost and increase the efficiency compared with the assembly of many components on a board (PCB). They also allow to reduce the size of the functionalities for mobile products with confined environments. The development of heterogeneous monolithic integration is more complex than the SiP one and requires one or two more technology generations to be launched. In order to increase system performance, the combination of SoC and SiP could become a cost-effective solution.  
System integrators are generally setting the pace for innovation for the electronics industry. The integrated circuit industry has also helped provide innovative technological building blocks to other industries that did not exist or were in their infancy before. Additional functionalities, such as MEMS, flat panel displays, sensors have been provided by the IC industry to other ones, which allowed for the development of the More than Moore (MtM) field, which began to be discussed in 2006 by the ITRS.
## 5 Deployment and Evaluation
### Single Tag Localization for Worker Tracking  
As we mentioned, safety tracking systems also track the location of workers. We also dedicated two scenarios for worker tracking. Figure 11 shows the two scenarios we used for worker tracking. Similar to vehicle tracking, we used a truck (in Figure 11b) to create obstructions in some scenarios. For this scenario, we placed a static tag (TAG #1) and two tags (TAG #2 and #3) traversing the trajectories marked with dashed line in Figure 11a,b.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pose Estimation** & **Estimated Error** & **Average Error (m)** & **Update Ratio (\%)** \\
**Method** & **Ratio (\%)** & **Average Error (m)** & **Update Ratio (\%)** \\ \hline Baseline & 31 & 2.8 & 98 \\ ViPER & 27 & 2.3 & 86 \\ ViPER+ & 26 & 1.4 & 99 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Pose estimation result for NLOS scenario. ViPER+ provided the same update rate as the baseline method while reducing the error rate and average error compared to the baseline.  
Figure 9: Results of vehicle tracking for scenarios with a blocking object. Using ViPER reduces the error rate in the pose estimation of the vehicle compared to the other two methods.  
Figure 11: Worker tracking for scenarios (**a**) without blocking objects and (**b**) with blocking object. Dashed lines represent the trajectory of each worker carrying a tag and the dashed circle shows the static tag.  
Figure 10: Pose estimation output for repetition #3 of LOS scenario (**a**) baseline, (**b**) ViPER, and (c) ViPER+.  
With ViPER+ for localization of the tags, we achieved the same update rate as the baseline method, while having a low error ratio and average error as ViPER.
# Remote Sensing of Sewing Work Levels Using a Power Monitoring System
## 3 Methodology
### Factors Affecting Performance in Garment Prodiciton Lines  
Time and quality, the most important productivity indicators, are closely related to \(L_{D}\) and \(L_{S}\) (Figure 1). In general, it is recognized that workers with a high \(L_{S}\) have a shorter working time and can produce better quality products (Figure 1a). In contrast, tasks with a high \(L_{D}\) take more time to complete and the resulting quality is likely to be lower (Figure 1b) [30].  
The garment production line basically consists of several sets of sewing machines and their operators, who use them to assemble raw materials into garments (Figure 2).  
Figure 1: Relationship between working time/quality and; (**a**) level of skill (\(L_{S}\)); (**b**) level of difficulty (\(L_{D}\)).  
Figure 2: Factors affecting performance in garment production lines.  
The performance of the production line (\(P_{L}\)), such as its output or total makespan, depends on the number of sewing machines and arrangement of operators (\(A_{O}\)) when the material (\(M\)) is supplied to the production line. At this time, \(M\) can be ignored because it is controlled so as not to affect the operation of the production line by establishing a separate supply plan. Therefore, \(P_{L}\) can be considered to be entirely dependent on \(A_{O}\) as follows:  
\[P_{L}=M\cdot A_{O}(\Sigma P_{O}) \tag{1}\]  
The value of \(A_{O}\) is a function of the performance of each individual operator (\(P_{O}\)). Therefore, \(P_{O}\) is an important factor in determining the influence of \(A_{O}\) on \(P_{L}\), and is affected by the task's \(L_{D}\), the operator's \(L_{S}\), and the condition of their machine (\(C_{M}\)). Note that \(L_{D}\) and \(L_{S}\) should be considered together in terms of time (\(L_{Dt}\), \(L_{St}\)) and quality (\(L_{Dq}\), \(L_{Sq}\)). The value of \(P_{O}\) is accordingly determined as follows:  
\[P_{O}=L_{D}\Big{(}L_{Dt}L_{Dq}\Big{)}L_{S}\Big{(}L_{St}L_{Sq}\Big{)}\cdot C_{M} \tag{2}\]  
In this paper, we propose a method for measuring \(L_{S}\) and \(L_{D}\) using a power monitoring system. In addition, we discuss a method for establishing a numerical criterion to apply the measured weights of \(L_{S}\) and \(L_{D}\) to \(A_{O}\) in order to ultimately improve \(P_{L}\).
## 4 Problem of the Study  
For many years, MNOs have focused continuously on QoS on the network level, so as to guarantee network quality as the main component of a good experience. Nonetheless, in recent years, there has been a paradigm shift to a service-oriented approach and toward customer experience management, where operators have focused on the services provided and on each client's experience. With the evolution of technology, the IoT, and MTC, the quality of device (QoD) will also be a fundamental concept of network monitoring.  
Service and device performances as perceived by IoT service providers and MNOs are dissimilar. While the former provides the end-user service (on the application layer), the latter puts in place a transport mechanism for connectivity between end-user devices. Thus, similar to OTT (over the top) services such as WhatsApp, Skype, and Viber, the content of the IoT services can also be encrypted with end-to-end encryption processes for data security. In this case, MNOs do not have access to the content of the IoT packets but provide the communication channels.  
By providing the transportation mechanism, MNOs can, through deep packet inspection (DPI), identify services by analyzing the patterns of traffic in the data packets, also known as the traffic signature. Figure 2 illustrates the communication concept, the traffic flow, and the data collection points in the network for this experimental study. The DPI engine allows the system to recognize IoTand MTC services based on their signature (packets traffic pattern) and not their content, as well as other standard Internet services.  
The heterogeneity and the volume of M2M device data make IoT service data processing much more complicated--on the one hand, because of the complexity of data streams and, on the other, because of the amount of intelligence required to detect the traffic patterns in the network. Depending on the service application, M2M system processes events in either real time or non-real time [23]--hence the need for big data technologies and cloud computing.
## 4 Computer Simulation Results
### Experimental Dataset
#### 4.1.2 MS-GLMB Filtering Parameters  
The 3D coordinates of the target source are obtained from the SRP-PHAT data. These 3D coordinates are used to construct the measurement set for the MS-GLMB filter. Table 3 outlines the parameters corresponding to the MS-GLMB filter. In this work, the same parameters of MS-GLMB filter are used as used in [6].  
The birth parameters of the 3 speakers can be denoted by \(\left\{r_{B}(l_{i}),p_{B}(\_,l_{i})\hat{=}\mathcal{N}(\cdot\mu_{B}^{(i)},p_{B}^ {(i)})\right\}_{i=1}^{3}\), where \(r_{B}(l_{i})\) denotes the birth probability of the \(i\)th speaker with label \(l_{i}\) and \(p_{B}(\_,l_{i})\) denotes the birth probability density of the \(i\)th speaker, which is a Gaussian with mean \(\mu_{B}^{(i)}\) and covariance \(P_{B}^{(i)}\). The Gaussian mean \(\mu_{B}^{(i)}\) represents a vector which contains the expected location of the birth of the \(i\)th speaker with label \(l_{i}\). The covariance \(P_{B}^{(i)}\) signifies the spatial uncertainty associated with the \(i\)th speaker. The estimated label corresponding to the target speaker is obtained by MS-GLMB filtering. The label information is helpful in demarcating the trajectories corresponding to the different speech sources within the environment, which in turn helps the algorithm to track the sources in motion across all the time frames.
# Review of Structural Health Monitoring Techniques in Pipeline and Wind Turbine Industries
## 2 Health Monitoring Techniques in Pipeline Industry
### In Pipeline Industry
#### 2.1.1 Optical Endoscopy  
Endoscopy: A boroscope and the use of CCTV are the means of visualizing the damage qualitatively. This image can be analyzed with advanced equipment to scan the contours in a topographic analysis and single out the defects. Laser ring triangulations and the visual odometry function with the deployment of crawlers mounted with equipment inside the pipeline is done [26; 27].  
Fiber Bragg Grating: These can be created using the property of variations of refractive indices of the core of an optical fiber, periodically. Utilizing predefined parameters, it is possible to predict the refractive indices at different places in the fiber with the variation of conditions of the environment. The light will result in constructive interference only at certain expected positions. There is a general relation for the wavelength variation [28; 29].  
\[\frac{\lambda_{B}}{\lambda}=(\alpha+\xi)\Delta T+(1-\rho_{e})\varepsilon \tag{1}\]  
\[t=\frac{rP}{\sigma}=\frac{rP}{\varepsilon E} \tag{2}\]where \(t=\) average wall thickness, \(r=\) average pipe radius, \(P=\) relative internal pressure, \(\sigma\) hoop stress, \(\xi=\) longitudinal strain, \(\xi=\) thermo-optic coefficient, \(\lambda_{B}=\) Bragg wavelength, \(\alpha=\) thermal expansion, \(T=\) temperature, \(\rho_{e}=\) photoelastic constant, and \(E=\) Young's modulus.
## 5 Conclusions  
For exploring the influences on forest and grass from the snow drought in the mid-high latitude area, logistic modeling and multisource remote sensing data were used to calculate the vegetation phenological periods in forest and grass over the past 20 years in the Sanjiang Plain. The results showed that snow drought events had happened more frequently and seriously in the mid-high latitude area in the last two decades; in the snow drought year, the SOS of the forest has been advanced ahead by six days and the EOS by five days; the threshold divided snowfall into two parts: above the 80% threshold, the SOS/EOS is prolonged by 8/6 days, and below the 80% threshold, the SOS/EOS comes early by 7/5 days. At the same time, the plant biomass GPP in the snow drought year was less 0.61% than that in the abundant year. These findings suggest the snow drought could influence the phenology and growth conditions in the mid-high latitude area.  
Snowfall is an essential water resource for plant growth and gives a contribution to the large water cycle. Especially in the mid-high latitude, snowfall will replace rainfall in the winter for a long time and makes an effect on the following growing season. This research first explored the threshold in the grass phenology to evaluate the snowfall and plant growth relationship; meanwhile, this study analyzed extreme snowfall in the mid-high latitude and gave help for assessing the continuing climate change evolutions. In the future, deeply understanding the regional vegetation growth features of climate event response will guide forestry and prataculture production and protect local vegetation ecosystems, where rapid climate change is projected to occur.  
Conceptualization, H.L. and M.C.; methodology, H.L. and M.C.; software, X.W.; validation, X.R., and X.W.; formal analysis, H.L.; investigation, X.W.; resources, H.L. and M.C.; data curation, H.L. and X.R.; writing--original draft preparation, H.L. and X.W.; writing--review and editing, X.R., P.W. and Y.G.; visualization, P.W. and Y.G.; supervision, S.Y. and M.C.; project administration, S.Y.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors would also like to thank the administrators at the Bawujiu Farm, and this research was funded by the National Natural Science Foundation of China (Grant Nos. U1812401, 41801334, U1603241, and 41875044), the National Key Project for R&D (Grant No. 2018 YFC0407900), the Fundamental Research Funds for the Central Universities (Grant No. 2017 NT11), and the Opening Fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection(Chengdu University of Technology) (Grant No. SKLGP2020K005).  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. See the Materials and Methods section for details.  
**Conflicts of Interest:** We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.
# Mechanical Properties and Formaldehyde Release of Particleboard Made with Lignin-Based Adhesives
###### Abstract  
The aim of this research was to evaluate the potential of magnesium lignocellulosic adhesive in particleboard manufacturing. Diphenylmethane diisocyanate (PMDI) between 1% and 3% and glucose (1% of the lignocellulosic content) were added as potential cross-linkers in the adhesive formulations. Mixed beech and spruce wood, 30% beech wood and 70% spruce wood, were employed for the configuration of the panel structure. The density, mechanical properties and formaldehyde emission of single-layer particleboard were investigated. Spectroscopic analysis (FTIR) revealed structural changes brought by oxidation that may indicate depolymerization by the splitting of C-O-C bonds and formation of carbonyl groups. Mechanical properties were improved, and the highest average values were recorded for panels having as adhesives oxidized lignin with cross-linkers as follow: 15 N/mm\({}^{2}\) (MOR), 3320 N/mm\({}^{2}\) (MOE) and 0.48 N/mm\({}^{2}\) (IB). The density profile presented higher values for faces in case of oxidized lignin panels. Changes were observed for oxidized lignin with cross-linker panels wherein the core had higher values. The results showed that the panels manufactured with adhesives composed of oxidized lignocellulosic (20% of the dried wood particles weight) and the addition of PMDI and glucose in various percentages have a positive influence on their formaldehyde release and mechanical properties requested by EN 312 (2004) standard.  
wood particleboard; magnesium lignocellulosicate; mechanical properties; formaldehyde emission; FTIR; bio-based adhesives; wood-based composites +
Footnote †: journal: Applied Sciences
## 3 Results  
### XRD Analyses  
The phase identification of the prepared Ca-SZ using XRD are shown in Figure 2A,B. The diffraction patterns of cubic ZrO\({}_{2}\) were obtained at 20 = 28\({}^{\circ}\), 41\({}^{\circ}\) and 47\({}^{\circ}\), corresponding to the Inorganic Crystal Structure Database (ICDS) no. 98-007-2021. Moreover, the tetragonal ZrO\({}_{2}\) was obtained at 20 = 30\({}^{\circ}\), 31.5\({}^{\circ}\), 34\({}^{\circ}\), 50\({}^{\circ}\), 60\({}^{\circ}\) and 62.7\({}^{\circ}\), corresponding to ICDS no. 98-010-8644. The zirconia was successfully stabilized with nano-CaO derived from cockle shells and from commercial source into tetragonal andcubic phases. The peaks at an angle 20 = 49\({}^{\circ}\) and 54\({}^{\circ}\) are assigned to the CaO structure corresponding to ICDS no. 01-073-5492. The same patterns were obtained for all samples at different sintering temperatures, which diverged only in the peak intensities. The increase in sintering temperature results in increasing the intensity of the cubic phase.  
Figure 2: (**A**) XRD patterns of calcia-stabilized zirconia with CaO derived from cockle shells (Ca-SZ(CS)) sintered for 2 h at (**a**) 1500 \({}^{\circ}\)C; (**b**) 1400 \({}^{\circ}\)C; (**c**) 1300 \({}^{\circ}\)C; (**d**) 1200 \({}^{\circ}\)C. (**B**) XRD patterns of calcia-stabilized zirconia with CaO derived from commercial CaO (Ca-SZ(CC)) sintered for 2 h at (**a**) 1500 \({}^{\circ}\)C; (**b**) 1400 \({}^{\circ}\)C; (**b**) 1300 \({}^{\circ}\)C; (**d**) 1200 \({}^{\circ}\)C.
# Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
## 2 Research Background
### Traditional AI vs. Explainable AI  
Traditional AI excels in achieving intelligent behavior and solving problems within well-defined domains. Techniques like rule-based systems, decision trees, and logic programming often deliver highly accurate and efficient results. These systems boast transparency, interpretability, and ease of maintenance due to their reliance on clear rules and logic. However, traditional AI struggles with adaptability and flexibility. Faced with new situations or complex, nuanced problems outside their predefined rules, these models can become brittle and prone to errors. Their limited ability to learn and adapt to new information further restricts their effectiveness in handling dynamic environments [17; 18; 19].  
Explainable AI focuses on making the internal workings and decision-making processes of AI models more transparent and interpretable to humans. It utilizes methods like feature importance analysis, LIMEs (Local Interpretable Model-Agnostic Explanations), and SHAPs (SHapley Additive exPlanations) to explain how AI models arrive at their predictions. Some of XAI's strengths are the increased trust and acceptance of AI systems by stakeholders, enabling the identification and mitigation of potential biases in AI models, and improved debugging and model improvement capabilities. However, like traditional AI, XAI also has limitations and weaknesses, such possibly being computationally expensive and resource-intensive, not all AI models can be fully explained, especially  
Figure 1: This figure explains the structure and organization of the paper.  
complex deep learning models, and explainability techniques may not always be readily understandable by non-experts [17; 18; 19].  
In essence, traditional AI prioritizes achieving the desired outcome, while XAI focuses on understanding how that outcome is achieved. While traditional AI remains valuable in specific applications, XAI is becoming increasingly important for building trust, ensuring fairness, and the responsible development of AI systems.