esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Framework for Integrating Cutting-Edge Technologies into Aircraft Health Management
#### 3.13.2 Technology Integration Layer
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Functionality** & \multicolumn{2}{c}{**IoT Actions**} & \multicolumn{2}{c}{**AI Actions**} & \multicolumn{2}{c}{**Blockchain Actions**} \\ \hline \hline \multirow{4}{*}{Data integration at MRO} & \multirow{4}{*}{-} & \multirow{4}{*}{Aggregates data from various flights for MRO analysis.} & - & Integrates diverse classes. & \multirow{4}{*}{-} & Provides a secure platform for integrating data from multiple sources. & \\  & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \hline \hline \multirow{4}{*}{Fleet-wide analysis} & \multirow{4}{*}{-} & \multirow{4}{*}{Enables collection and aggregation of fleet-wide data for analysis.} & - & \multicolumn{2}{c}{Performs predictive analytics to forecast maintenance needs.} & - & \multicolumn{2}{c}{Secures fleet-wide data analysis results.} \\  & & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \hline \multirow{4}{*}{Predictive maintenance} & \multirow{4}{*}{-} & \multirow{4}{*}{Monitors system health to predict hardware failures.} & - & Uses historical data to model and predict future failures. & \multirow{4}{*}{-} & \multirow{4}{*}{-} & \multirow{4}{*}{Ensures that maintenance records are immutable and accessible for audit.} \\  & & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \hline \hline \multirow{4}{*}{Maintenance scheduling} & \multirow{4}{*}{-} & \multirow{4}{*}{Assists in scheduling by providing real-time operational status.} & - & Determines optimal maintenance times using AI optimization algorithms. & \multirow{4}{*}{-} & Records and verifies scheduled maintenance against actual actions. \\  & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \hline \multirow{4}{*}{Regulatory compliance} & \multirow{4}{*}{-} & \multirow{4}{*}{Ensures all sensor and operational data needed for compliance are collected accurately.} & - & Analyzes data to ensure compliance with safety and operational standards. & \multirow{4}{*}{-} & Provides a secure and immutable record of all compliance-related actions and data. \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \hline \multirow{4}{*}{Decision support} & \multirow{4}{*}{-} & \multirow{4}{*}{Provides real-time data inputs for decision-making processes.} & - & Analyzes real-time data to provide actionable insights and recommendations. & \multirow{4}{*}{-} & \multirow{4}{*}{-} & \multirow{4}{*}{Secures and records decision-making processes and outcomes.} \\ \cline{1-1}  & & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: _Cont._This table demonstrates how the IoT, AI, and Blockchain work synergistically to enhance decision-making capabilities in aircraft health management, improving safety, efficiency, and compliance across all operations.
## 4 Conclusions  
1. Phthalic anhydride can undergo an acylation reaction with PPTA fiber, introducing -OH active functional groups onto the phenyl rings of the fibers. Some of the amide bonds on the fibers undergo hydrolysis to form -COOH, increasing the O/C elemental ratio on the fiber surface. With increase in the concentration of phthalic anhydride, the O/C ratio initially increases and then decreases. When the concentration of pyromellitic anhydride is 0.3 mol/L, the O/C ratio reaches its maximum value of 0.176, representing an 18.12% increase compared to unmodified PPTA fiber.
2. After modification with 0.3 mol/L phthalate anhydride, the tensile strength, flexural strength, and interlaminar shear strength of the PPTA/DGEBA composite materials all increased to their maximum values, showing improvements of 17.94%, 44.18%, and 15.94%, respectively, compared to unmodified specimens.
3. Anhydrorous AlCl3 can serve as a catalyst for the acylation reaction between phthalic anhydride and PPTA fiber, reducing the modification time and increasing the introduction rate of oxygen-containing functional groups on the fiber surface, resulting  
Figure 17: Lateral microscopic morphology of catalytically modified PPTA/DGEBA composites after bending damage:(**a**) PPTA-0.5h (A); (**b**) PPTA-1 h (A); (**c**) PPTA-1.5 h (A); (**d**) PPTA-2 h (A).  
in an increase in the O/C elemental ratio on the fiber surface. After catalytic modification for 0.5 h, 1 h, 1.5 h, and 2 h, the O/C elemental ratio on the fiber surface shows an increasing trend. Compared to PPTA fiber modified without the addition of catalyst, the O/C elemental ratio increased by 74.43%, 121.02%, 101.70%, and 80.68%, respectively.
4. After catalytic modification for 0.5 h, the tensile strength, flexural strength, and interlaminar shear strength of the PPTA/DGEBA composite materials all increased to their maximum values. Compared to non-catalyzed modified samples, there were improvements of 32.28%, 24.91%, and 29.10%, respectively.  
Conceptualization, Y.X. and X.Q.; methodology, Y.X. and Y.E.; investigation, Y.E. and G.X.; data curation, H.G. and H.L.; writing--original draft preparation, Y.E. and Y.X.; writing--review and editing, Y.X.; supervision, X.Q.; project administration, Y.X. and X.Q.; funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Defence Science and Technology Base Strengthening Programme of China and the China Construction Corporation Technology R&D Plan (CSCEC-2020-Z-1).  
Not applicable.  
The authors gratefully acknowledge the financial support provided by the Defence Science and Technology Base Strengthening Programme of China and the China Construction Corporation Technology R&D Plan (CSCEC-2020-Z-1, CSCEC-2022-Z-8).  
The authors declare no conflicts of interest.
###### Abstract  
Supporting education and training initiatives has been identified as an effective way to address Sustainable Development Challenges. In this sense, e-learning stands out as one of the most viable alternatives considering its advantages in terms of resources, time management, and geographical location. Understanding the reasons that move users to adopt these technologies is critical for achieving the desired social objectives. The Technology Acceptance Model (TAM) provides valuable guidelines to identify the variables shaping users' acceptance of innovations. The present study aims to validate a TAM extension designed for FARMER 4.0, an e-learning application in the agricultural sector. Findings suggest that content quality (CQ) is the primary determinant of farmers' and agricultural entrepreneurs' perception of the tool's usefulness (FU). Furthermore, experience (EXP) and self-efficacy (SE) shape potential users' perceptions about ease of use (PEOU). This study offers helpful insight into the design and development of e-learning applications in the farming sector and provides empirical evidence of TAM's validity to assess technology acceptance.  
TAM; e-learning applications; agriculture; technology acceptance +
Footnote †: journal: Applied Sciences  
Article  
Validation of a TAM Extension in Agriculture: Exploring the Determinants of Acceptance of an e-Learning Platform
## 2 Hybrid System and Techno-Economic Analysis Method
### The Design of Hybrid System Components
#### 2.2.2 PV Tracking Techniques  
PV tracking techniques are the mechanism to adjust solar panels toward sun's rays for improving productivity and efficiency of the PV system through maximum utilization of solar irradiation. Commonly the fixed-tilt solar panel uses manually adjustable panel slops, and the advantages of this kind of PV installation is simplicity and low cost. However, they have a significant shortage to utilize the maximum possible of solar irradiation since the sun changes position throughout a day or a seasonal orbit [14]. PV tracking techniques can make a PV array automatically flexible to adapt toward high-density rays from the sun. Whether single-axis or two-axis tracker, the PV tracker needs additional components and accessories such as, motor, gear, control unit, and sensor that will increase the cost of the PV system [57]. The major four PV tracking techniques used in this study are:  
* Fixed-tilt solar panel (FMSP).
* PV array is set fixed-tilt angle and azimuth. This is the commonest and simplest technique.
* Horizontal single-axis tracker (HSAT).
* The PV array is installed to rotate around the horizontal (east-west) axis. The tilt angle adjusts continuously for matching the angle of incidence, while azimuth is fixed.
* Vertical single-axis tracker (VSAT).
* PV array is installed to rotate around the vertical (north-south) axis. The tilt angle is fixed, while azimuth is adjusted simultaneously for matching the angle of incidence.
* Dual-axis tracker (DAT).  
PV array is installed to rotate in both horizontal and vertical. Therefore, the incidence angle is always perpendicular to the surface of the panels.  
The economic cost of horizontal, vertical and dual-axis tracker are stated in Table 1, and the four PV tracking techniques are presented in Figure 2[56].
## 2 Materials and Methods
### Analysis  
#### 2.3.1 Landsat Time Series  
The methodological workflow is summarized in Figure 2. Processing began by applying scaling factors to convert pixel values to reflectance, ensuring consistency across imaging sessions and sensors [75]. Saturated pixels were then identified and removed [80; 81], followed by masking and removing clouds and cloud shadows using the Quality Assessment bands [81]. Landsat imagery was then reprojected to match the lidar coordinate system (NAD83 CSRS, UTM Zone 12 North) for spatial alignment.  
To mitigate cloud and shadow influences within the Landsat image time series, median image compositing was applied to create annual summertime composites [82; 83; 84; 85]. To remove the short-term influence of lowering or increasing water levels (i.e., drying and/or flooding) from the VI trend analyses, all open water lakes and channels were masked out of the study polygons (e.g., Ju and Masek [86]) using Modified Normalized Difference Water Index (MNDWI), which is the ratio of the (green--SWIR) and (green + SWIR) [87]. Based on visual assessments, pixel MNDWI values > 0.1 were selected to define a binary water mask, and all such areas were excluded from further analysis.  
NDVI and EVI were used as proxies for vegetation greenness and canopy structure. NDVI, calculated as (NIR \(-\) Red)/(NIR + Red), is sensitive to chlorophyll content [19], while EVI = 2.5 \(\times\) (NIR \(-\) Red)/(NIR + 6 \(\times\) Red \(-\) 7.5 \(\times\) Blue + 1) adjusts for atmospheric and background effects using the blue band, improving performance in dense vegetation [20; 21]. NDVI and EVI were created for each annual composite in Google Earth Engine [88]. VI trends were assessed using Theil-Sen median trend analysis [89; 90], a robust non-parametric method that is minimally affected by outliers [91; 92; 93; 94; 95]. The procedure calculates the slope of the line connecting medians for each pair of points in the dataset [93; 96]. The direction of the Theil-Sen slope indicates vegetation changes where positive when \"greening\" (increase in vegetation) and negative when signifying \"browning\" (decline in vegetation), and it can be interpreted as the rate of change trend over time [90]. Only Theil-Sen slopes with \(p\)-values below 0.05 and Z-scores exceeding 1.96 were considered significant, ensuring a minimum 95% confidence level [93; 97; 98] (Figure 2).  
Summertime LST (\({}^{\circ}\)C) was obtained from Landsat thermal bands (Band 6 for Landsat 5 and 7, and Band 10 for Landsat 8) [99; 100], and Theil-Sen slopes of the LST time series were extracted for all land pixels. These trends were assumed to be proxies for changing proportions of sensible and latent heat exchange following change in soil surface saturation or the underlying shallow groundwater table, as areas of nearby open water fluctuate [101; 102]. In general, an increasing LST trend is assumed to show a drying soil surface as more of the available energy goes into sensible heating of the ground surface instead of the latent heat of water evaporation.
# Steel Surface Defect Detection Algorithm Based on Improved YOLOv8n
###### Abstract  
The traditional detection methods of steel surface defects have some problems, such as a lack of feature extraction ability, sluggish detection speed, and subpar detection performance. In this paper, a YOLOv8-based DDI-YOLO model is suggested for effective steel surface defect detection. First, on the Backbone network, the extended residual module (DWR) is fused with the C2f module to obtain C2f_DWR, and the two-step approach is used to carry out the effective extraction of multiscale contextual information, and then fusing feature maps formed from the multiscale receptive fields to enhance the capacity for feature extraction. Also based on the above, an extended heavy parameter module (DRB) is added to the structure of C2f_DWR to make up for the lack of C2f's ability to capture small-scale pattern defects between training to enhance the training fluency of the model. Finally, the Inner-IoU loss function is employed to enhance the regression accuracy and training speed of the model. The experimental results show that the detection of DDI-YOLO on the NEU-DET dataset improves the mAP by 2.4%, the accuracy by 3.3%, and the FPS by 59 frames/s compared with the original YOLOv8n.Therefore, this paper's proposed model has a superior mAP, accuracy, and FPS in identifying surface defects in steel.  
defect detection; YOLOv8n; C2f_DWR module; C2f_DRB module; inner-IoU loss function; feature fusion +
Footnote †: journal: Applied sciences
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scoring** & **FM** & **STD** & **TLP** & **11 sA4 Null** & \\  & **Control** & **SBM** & **SBM** & **SBM** & **\(p\)-Value** \\ \hline Goblet cells & 1.9 \(\pm\) 0.3 b & 3.2 \(\pm\) 0.4 a & 3.4 \(\pm\) 0.5 a & 3.7 \(\pm\) 0.5 a & \(<\)0.001 \\ Lamina propria & 1.8 \(\pm\) 0.4 b & 3.2 \(\pm\) 0.4 a & 3.5 \(\pm\) 0.5 a & 3.6 \(\pm\) 0.5 a & \(<\)0.001 \\ Mucosal fold & 1.9 \(\pm\) 0.5 b & 2.7 \(\pm\) 0.7 ab & 2.9 \(\pm\) 0.6 a & 3.3 \(\pm\) 0.7 a & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Semiquantitative scoring system (mean \(\pm\) SD) of three parameters based on [10]: (1) the appearance and length of mucosal folds (MF); (2) the degree of widening of the lamina propria (LP); (3) the abundance of goblet cells (GC). For each of these parameters a score was given on a scale of 1 to 5. An increasing score value represents a greater degree of intestinal damage. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Blood Chemistry Parameters** & \multicolumn{1}{c}{**FM**} & \multicolumn{1}{c}{**STD**} & \multicolumn{1}{c}{**TLP**} & \multicolumn{1}{c}{**11 sA4 Null**} & \\  & **Control** & **SBM** & **SBM** & **SBM** & **\(p\)-Value** \\ \hline Albumin (g L\({}^{-1}\)) & 20.3 \(\pm\) 0.6 ab & 20.0 \(\pm\) 1.0 b & 22.3 \(\pm\) 1.1 a & 20.7 \(\pm\) 0.6 ab & 0.044 \\ Alkaline phosphatase (U L\({}^{-1}\)) & 21.3 \(\pm\) 6.1 & 18.7 \(\pm\) 4.7 & 24.3 \(\pm\) 2.1 & 15.7 \(\pm\) 5.7 & 0.242 \\ Anion gap (mmol L\({}^{-1}\)) & 59.3 \(\pm\) 3.5 & 56.0 \(\pm\) 1.0 & 53.0 \(\pm\) 2.6 & 58.0 \(\pm\) 8.7 & 0.463 \\ AST (U L\({}^{-1}\)) & 301.3 \(\pm\) 53.6 & 301.7 \(\pm\) 28.7 & 359.5 \(\pm\) 2.1 & 315.7 \(\pm\) 31.5 & 0.361 \\ Bicarbonate (mmol L\({}^{-1}\)) & 6.3 \(\pm\) 0.6 & 5.3 \(\pm\) 1.1 & 5.7 \(\pm\) 0.6 & 6.7 \(\pm\) 2.1 & 0.577 \\ Chloride (mmol L\({}^{-1}\)) & 123.3 \(\pm\) 8.7 & 120.3 \(\pm\) 1.5 & 125.3 \(\pm\) 5.1 & 120.0 \(\pm\) 1.0 & 0.561 \\ Cholesterol (mmol L\({}^{-1}\)) & 13.2 \(\pm\) 2.9 & 9.8 \(\pm\) 1.1 & 12.4 \(\pm\) 0.9 & 11.9 \(\pm\) 1.4 & 0.236 \\ Creatine kinase (U L\({}^{-1}\)) & 7286 \(\pm\)1425 & 7992 \(\pm\) 1198 & 7446 \(\pm\) 1719 & 8089 \(\pm\) 1674 & 0.890 \\ Globulin (g L\({}^{-1}\)) & 18.7 \(\pm\) 2.1 & 18.7 \(\pm\) 1.1 & 21.0 \(\pm\) 1.0 & 20.3 \(\pm\) 0.6 & 0.142 \\ Glucose (mmol L\({}^{-1}\)) & 5.2 \(\pm\) 0.8 & 4.2 \(\pm\) 0.3 & 4.2 \(\pm\) 0.2 & 4.0 \(\pm\) 0.4 & 0.055 \\ Phosphate (mmol L\({}^{-1}\)) & 3.1 \(\pm\) 0.3 & 2.7 \(\pm\) 0.3 & 2.6 \(\pm\) 0.3 & 2.5 \(\pm\) 0.3 & 0.103 \\ Potassium (\(\mu\)mmol L\({}^{-1}\)) & 4.2 \(\pm\) 0.6 & 4.1 \(\pm\)0.6 & 4.1 \(\pm\) 0.5 & 4.0 \(\pm\) 0.4 & 0.963 \\ Protein (g L\({}^{-1}\)) & 39.0 \(\pm\) 2.6 ab & 38.7 \(\pm\) 1.5 b & 43.3 \(\pm\) 1.5 & 41.0 \(\pm\) 1.0 ab & 0.042 \\ Ratio & 1.1 \(\pm\) 0.1 & 1.1 \(\pm\) 0.1 & 1.0 \(\pm\) 0.1 & 0.4 \(\pm\) 0.0 & 0.344 \\ Sodium (mmol L\({}^{-1}\)) & 184.7 \(\pm\) 11.6 & 177.7 \(\pm\) 0.6 & 179.7 \(\pm\) 3.0 & 180.3 \(\pm\) 5.1 & 0.683 \\ Triglyceride (mmol L\({}^{
# An Overview of the Process Mechanisms in the Laser Powder Directed Energy Deposition
## 3. Melt Pool Generation Mechanisms
### Thermal Behaviour
#### 3.1.2 Effects of the Powder Flow Rate  
The powder flow rate represents the amount of additional material introduced in the deposition area. Contradictory results exist in the literature on the effect of powder flow rate on the width of the melt pool. [PERSON] and [PERSON] [45], [PERSON], et al. [100], and [PERSON], et al. [105], varied the powder flow rate and demonstrated that the layer width was not influenced by this parameter. During the deposition of Ni20 powder on 316 L substrate, [PERSON], et al. [97] showed that the melt pool temperature decreases with increasing the powder flow rate, as depicted in Figure 20. Therefore, it was found that increasing the powder flow rate results in a smaller melt pool area [106]. The reduction of melt pool size was confirmed by [PERSON], et al. [99] during the production of Ti-48 Al-2 Mn-2 Nb rectangular strips. The reduction in melt pool was attributed to the increase in laser attenuation due to the increase in powder flow rate. Conversely, [PERSON], et al. [98] and [PERSON], et al. [85] observed that the melt pool width slightly increased with the powder flow rate. Consequently, the effect of powder flow rate on melt pool width depends strongly on the value of laser power and could not be studied independently [96].  
Figure 19: Effect of laser power on average temperature distribution into the melt pool (reproduced with permission from [104]; Copyright © 2012 Springer Nature).  
On the other hand, the layer height increases linearly with increasing powder flow rate [45; 98; 100; 107; 108; 109]. [PERSON], et al. [99] observed that the layer height increases with the powder flow rate; however, after reaching an upper critical value of the powder flow rate, a reduction in layer height is observed. The existence of an upper limit of the powder flow rate that depends on the size of the melt pool should be considered. If the powder flow rate exceeds this value, the incoming particles bounce off those particles in the melt pool, creating the self-shielding effect [110].
# 2D/3D Multimode Medical Image Registration Based on Normalized Cross-Correlation
###### Abstract  
Image-guided surgery (IGS) can reduce the risk of tissue damage and improve the accuracy and targeting of lesions by increasing the surgery's visual field. Three-dimensional (3D) medical images can provide spatial location information to determine the location of lesions and plan the operation process. For real-time tracking and adjusting the spatial position of surgical instruments, two-dimensional (2D) images provide real-time intraoperative information. In this experiment, 2D/3D medical image registration algorithm based on the gray level is studied, and the registration based on normalized cross-correlation is realized. The Gaussian Laplacian second-order differential operator is introduced as a new similarity measure to increase edge information and internal detail information to solve single information and small convergence regions of the normalized cross-correlation algorithm. The multiresolution strategy improves the registration accuracy and efficiency to solve the low efficiency of the normalized cross-correlation algorithm.  
2D/3D medical image registration; normalized cross-correlation; differential operator; image processing +
Footnote †: journal: Applied sciences
# Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks +
###### Abstract  
In this paper, we focus on the radio resource planning in the uplink of licensed Orthogonal Frequency Division Multiple Access (OFDMA) based Internet of Things (IoT) networks. The average behavior of the network is considered by assuming that active sensors and collectors are distributed according to independent random Poisson Point Process (PPP) marked by channel randomness. Our objective is to statistically determine the optimal total number of Radio Resources (RRs) required for a typical cell. On one hand, the allocated bandwidth should be sufficiently large to support the traffic of the devices and to guarantee a low access delay. On the other hand, the over-dimensioning is costly from an operator point of view and induces spectrum wastage. For this sake, we propose statistical tools derived from stochastic geometry to evaluate, adjust and adapt the allocated bandwidth according to the network parameters, namely the required Quality of Service (QoS) in terms of rate and access delay, the density of the active sensors, the collector intensities, the antenna configurations and the transmission modes. The optimal total number of RRs required for a typical cell is then calculated by jointly considering the constraints of low access delay, limited power per RR, target data rate and network outage probability. Different types of networks are considered including Single Input Single Output (SISO) systems, Single Input Multiple Output (SIMO) systems using antenna selection or Maximum Ratio Combiner (MRC), and Multiuser Multiple Input Multiple Output (MU-MIMO) systems using Zero-Forcing decoder.  
Keywords:LPWAN; licensed OFDMA-based IoT; resource planning; stochastic geometry +
Footnote †: This paper is an extended version of our paper published in: [PERSON]; [PERSON]; [PERSON]; [PERSON] Statistical Uplink Dimensioning in Licensed Cellular Low Power IoT Networks. In Proceedings of the 2018 25 th International Conference on Telecommunications (ICT), St. Malo, France, 26-28 June 2018.