esa-sceva/satcom-chunk-collection huggingface dataset:
# Rapid Target Extraction in LiDAR Sensing and Its Application in Rocket Launch Phase Measurement
## 2 Principle and Methods
### Principle of Rapid Target Point Cloud Extraction Method
#### 2.1.2 Image Filtering
After applying the mask, the inverse Fourier transform is performed on the modified frequency domain image, resulting in a grayscale image I\({}^{\prime}\) with most of the background removed. The mathematical expression for this operation is as follows:  
\[\text{I}_{(i,j)}\overset{\mathcal{F}}{\rightarrow}\hat{\text{I}}_{(i,j)} \bigodot\text{M}_{\mathcal{F}(i,j)}\overset{\mathcal{F}-1}{\rightarrow}\text{ I}^{\prime}_{(i,j)} \tag{14}\]  
Here, \"\(\odot\)\" represents the matrix convolution operation. This approach allows for retaining the target point cloud data while filtering out most of the background point clouds, thus reducing the size of the image data, as shown in Figure 3b. Additionally, it helps improve the speed of subsequent target extraction.
# GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques
## 4 I/O Detection Scheme
### Training Phase  
Since the input data are well collected, the training phase is easier, which leads to high accuracy in the model. Errors will fall to the semi-indoor environment, where the GPS signal is still strong despite the indoors environment. This is the characteristic of the input data, yet the learning model can reduce the impacts to minimum.  
We employed L2 norm for error calculation, where errors are calculated as the Euclidean distances between two points. Note that L2 norm is the best choice for multidimensional input vector data.  
Based on the required accuracy and user devices, we can also apply offline training. Offline training is suitable for low-accuracy applications or devices that do not require permanent mobility  
Figure 10: Neural network model.  
because it does not require significant resources, has a fast calculation process, and does not require an update operation. Offline learning is also an advantage of this scheme because the final model is very simple, offers fast calculation, and requires only the current GPS signal information. In contrast, online training can increase the accuracy of the model; however, online training results in a complex model and requires extra calculations to update the model. Online training is more suitable for mobile applications, where the device's resources are available and applications requires high accuracy.  
In this study, we focus on offline training, and we tested with many machine learning techniques to identify the best solution for the GPS signal classifier.
## 2 Design of the Acoustic Black Hole Plate  
A schematic diagram of the structure of a 2D ABH embedded into a rectangular plate is given in Figure 1. The following equation can be used to calculate the thickness of the ABH plate:  
\[h(y)=\left\{\begin{array}{cc}\varepsilon y^{m}-b,&\text{ABH region}\\ h_{p},&\text{Uniform region}\end{array}\right. \tag{1}\]  
where \(\varepsilon\) is a constant, \(m\) is the power exponent of the thickness change of the ABH region, \(m\geq 2\), \(b\) is the distance between the coordinate origin and the centre of the ABH region in the \(z\)-axis direction, and \(h_{p}\) is the thickness of the uniform region of the ABH plate. However, because the thickness of the end tip location of the acoustic black hole cannot be lowered to zero with a power index greater than 2, there is always a truncation in the real ABH structure. The thickness of the truncated region in the model shown in Figure 1 can be stated as follows  
\[h_{t}=\frac{h_{p}}{2}-b \tag{2}\]  
The speed of propagation of the flexural wave in an isotropic thin plate is as follows [35]:  
\[c_{B}=\left(B\omega^{2}/m\right)^{1/4} \tag{3}\]  
where \(B\) is the bending stiffness of the ABH plate material, \(B=Eh(x)^{3}/[12(1-\
u^{2})]\), \(\omega\) is the angular frequency, \(m\) is the areal density of the ABH plate, \(m=\rho_{m}h(x)\), \(E\) is the Young's modulus of the ABH plate material, \(\
u\) is the Poisson's ratio, and \(\rho_{m}\) is the density of the ABH plate material.  
The 2D ABH rectangular plate designed in this paper contained four ABH regions with the same diameter and truncation thickness, as shown in Figure 2. The ABH plate has a length of 450 mm, a width of 300 mm, and a uniform thickness area thickness of 4 mm. For the ABH with the centre of \(O_{2}\) in Figure 2, its thickness equation is \(h(y)=0.0014\times(x-80)^{2}-1.5\). According to Equations (1) and (2), the thickness of the truncation is 0.5 mm, and the maximum diameter of the ABH region is 100 mm. The \(\varepsilon\), \(m\), and \(b\) in the thickness equations of the remaining three ABHs are the same as the thickness equation of the ABH with the centre \(O_{2}\), only the positions of their centre points are different.  
Figure 1: Schematic diagram of a rectangular plate embedded with 2D ABH.
# A Multi-Layer Secure Sharing Framework for Aviation Big Data Based on Blockchain
## 3 Analysis of ATM Big Data Sharing
### ATM System Concepts
#### 3.1.4 Constraints
Through measures such as building international cooperation mechanisms, improving information sharing platforms, and optimizing incident response procedures, the strategy aims to comprehensively enhance the cyber resilience of aviation systems. The 2022 revision to Annex 17--Aviation Security of the Convention on International Civil Aviation [28] further refined technical specifications, mandating that member states establish full lifecycle risk management mechanisms covering critical aviation ICT systems and data based on the Secure by Design principle, with emphasis on ensuring the confidentiality, integrity, and availability of critical systems and data. Building on this foundation, the latest edition of the Global Aviation Security Plan (GASeP) [29] proposed more actionable implementation measures, calling for the establishment of a cross-domain, multi-layered cybersecurity protection system encompassing key areas including air traffic management, communications, surveillance, and aircraft operations. The plan sets clear phased targets through detailed implementation roadmaps: by 2030, over 90% of member states must achieve effective implementation in aviation cybersecurity to ensure continuous availability of critical systems, thereby constructing a highly resilient global aviation cybersecurity protection framework. Through coordinated management of these multidimensional constraints, global ATM systems can achieve efficient, sustainable, and interference-resistant operations while maintaining safety.
## 3 Simulation Examples
### Residual STFT-Based Mode Shape Extraction
#### 3.2.4 Effect from the Beam Damping Property
However, due to the involvement of internal damping, the modal damping ratio will be larger at the higher mode. With the third modal damping ratio (\(\xi_{3}\) = 0.0341), there are remarkable derivations from the analytical mode shape for Case 2 (without traffic flow) with a MAC value of 0.824. When traffic flow is taken into consideration, Case 5 could still provide the mode shapes with a MAC value larger than 0.92. When the third modal damping ratio comes to \(\xi_{3}\) = 0.0910, the mode shapes extracted from Case 3 (without traffic flow) are distorted with MAC values of less than 0.73. At the same time, Case 6 (with traffic flow) could keep the MAC values larger than 0.96. Figure 10: The first three beam mode shapes using residual STFT-based extraction under different beam damping properties without traffic flow are compared to the analytical first mode shape (a), the second mode shape (b), and the third mode shape (c), as well as the MAC values provided in brackets.
## 2 Materials and Methods
### Methodology
#### 2.2.2 Chemical Activation Process Methods  
This study referred to the activation process methods of other volcaniclastic materials [44; 45; 46]. CaO and Ca(OH)\({}_{2}\) were selected for the alkaline activation process for two reasons: first, the natural volcanic rock powders in the Hotan area of Xinjiang were inherently calcium deficient, with a CaO content of only 6.96%, and they required Ca\({}^{2+}\) to form gelling hydration products; second, the natural volcanic rock powders contained acidic oxides, which made them more reactive in an alkaline environment. Additionally, CaSO\({}_{4}\) and gypsum (CaSO\({}_{4}\) -2H\({}_{2}\)O) were chosen to introduce SO\({}_{4}\)\({}^{2-}\), which is essential for ettrinigite formation, although ettrinigite's expansiveness necessitated a reasonable dosage. Lastly, Na\({}_{2}\)SO\({}_{4}\) and CaCO\({}_{3}\) were selected. Na\({}_{2}\)SO\({}_{4}\) addition increased both Na\({}^{+}\) and SO\({}_{4}\)\({}^{2-}\) in the system; research indicated [47] that CaCO\({}_{3}\) accelerated C\({}_{3}\)S hydration, offered attachment sites, and facilitated ettrinigite generation within the structure.  
Chemical reagent doping test program Ca(OH)\({}_{2}\) (H2-H4): 3%, 4%, and 5%; Na\({}_{2}\)SO\({}_{4}\) (H5-H7): 3%, 4%, and 5%; CaCO\({}_{4}\) (H8-H10): 2%, 3%, and 4%; CaO (H11-H13): 2%, 4%, and 6%; CaCO\({}_{3}\) (H14-H16): 2%, 4%, and 6%; CaCO\({}_{4}\); CaCO\({}_{4}\);2H\({}_{2}\)O (H17-H19): 2%, 4%, and 6%. There were 18 schemes in total. In addition, the pure cement group (H-0) and cement + 30% volcanic rock powder (H-1) were set as the blank test group, and the specific surface area of volcanic rock powder was 480 m\({}^{2}\)/kg. In accordance with the method in the Chinese standard GB/T1/671-2021 [43], prismatic cemented sand specimens with dimensions of 40 mm \(\times\) 40 mm \(\times\) 160 mm were prepared with a water-cement ratio of 0.5. The dosage of volcanic rock powder was 30% of the mass of cement, and the chemical reagent dosage was the mass percentage of cement + 30% volcanic rock powder. After standard curing, the flexural and compressive strengths were tested at 3 d, 7 d, and 28 d ages, and the flexural and compressive activity indices were calculated. Based on the results, the optimum three chemicals were selected for a composite orthogonal test. The orthogonal test factor levels are shown in Table 4.  
In the chemical composite formulation, the optimum test group (HS4), selected based on orthogonal tests, the cement + 30% volcanic rock powder (H-1) blank test group, and the H0 pure cement group were used to determine the pore size distribution volume of the hardened slurries at the ages of 7, 28, and 60 days. Volume percentages were calculated and determined by using a Mack AutoPore IV 9500 mercuric piezometer (Mack Instruments, Cincinnati, OH, USA). SEM electron microscope scans and XRD microtests were also carried out to analyze the micro-mechanism of the elevated activity of the chemical excitants. XRD data were collected at room temperature using Cu-K\(\alpha\) radiation (\(\lambda\) = 1.5406 A) operated in a reflective geometry (0/20).
## 1 Introduction
As well as the reconstitution of training delivery systems, previous studies show that, for this technology to become a reality in higher education, attitudes towards IoT and material access. So, that educational and income differences, need to be taken into account and not generate inequalities and therefore a possible rejection of its implementation [3]. Another factor that affects its dissemination is the level of training. Thus, people with higher education, like those with higher incomes, have more positive attitudes and are the first to actually buy IoT technology [29]. This also means that they are the first to develop the necessary skills and engage in diverse use of IoT [30]. Among the potential benefits that IoT can offer to education are improved campus security, efficient facility energy systems, customized training plans accessible at any time in a ubiquitous and context-aware learning environment [31], improved student performance and increased accountability through data collection and subsequent analysis to find more efficient ways of teaching and learning [32]. The use of this technology provides deeper learning experiences, as it changes the way that we all learn and engage in learning activities, allowing for students to participate in their own learning and content creation and helping teachers to deliver personalized content that improves student outcomes [26]. Likewise, the implementation of IoT in universities offers opportunities and challenges. The technologies implied by IoT promote the development of an IoT society and the promotion of a new digital culture, where the opportunities to obtain online degrees and easy access to learning content in both structured and unstructured formats are pushing higher education institutions to make a digital turnaround that represents a drastic change in the traditional educational paradigm [25]. The challenges include the need for access to training content on demand at any time, the use and development of mobile applications for teaching and learning [33], ensuring the security of devices, services and the privacy of the IoT ecosystem [34], ensuring the quality of training and assessment of students, and, finally, seeking alternative strategies and resources to maintain the high cost of maintaining the IoT technology itself [35]. Based on these ideas, the purpose of this study was to explore the acceptance of the IoT by university professors for future adoption in higher education. The following research question was followed:  
RQ: Do the factors of the UTAUT model influence the acceptance of the IoT in higher education?
# Polarization Radiometric Calibration in Laboratory for a Channeled Spectropolarimeter
## 1 Introduction
Formerly, we proposed a model of self-correction with a 3:1 ratio of retarders to calibrate and compensate alignment errors and retardations simultaneously by measuring the SOP of target light in Ref. [17]. In order to maintain the advantages of this calibration method, the polarization radiometric calibration model proposed in this paper still adopts the scheme with a 3:1 ratio of retarders. Based on this model, we present a reconstruction method of Stokes parameters and a series of parameters calibration methods, classical radiometric calibration, self-correction technique, polarization radiometric decoupling calibration, and so on. By using the presented methods, the radiometric calibration coefficient and polarization parameters are determined accurately, and the reconstruction accuracy of the polarization information could be improved obviously, which is significant for the practical quantitative applications of the fieldable CDISP. This paper is organized as follows. Section 2 provides the principle of fieldable channeled dispersive imaging spectropolarimetry, and then, derive a theoretical model for the polarization radiometric calibration and the reconstruction method of Stokes parameters. Section 3 proposes a series of calibration methods of parameters in the polarization radiometric calibration model. In Section 4, we analyze and summary our simulation experiment results. The conclusion is presented in Section 5. Establishment of Polarization Radiometric Calibration Model of the Fieldable Channeled Dispersive Imaging Spectropolarimetry  
In this section, we first introduce the principle of fieldable channeled dispersive imaging spectropolarimetry and its four sub-modules. Then, according to modularity theory and the principle of classical radiometric calibration, we derive a theoretical model for the polarization radiometric calibration.
## 2 Results and Discussion  
### Volatomic Fingerprinting from Spices  
Due to high demand, nutritional value, and the _sui generis_ typical aroma highly appreciated by consumers, a volatitomic-based analytical approach was used to establish the volatitomic fingerprint of each spice as a useful strategy to assess and define its quality and authenticity. Figure S1 (Supplementary Material) shows the typical chromatograms of the investigated spices, obtained by HS-SPMEDWB/CAR/PDMS/GC-qMS. Their chromatographic profiles show that the concentration range of the volatile compounds differs enormously for the various spices. For instance, black pepper presents a very complex chromatogram with most volatiles at high concentrations (based on peak area intensity), whereas the volatile compounds identified in sweet paprika shows relatively low concentrations (Figure S1). In total, 162 VOMs were identified, most of them in black pepper (**69**), while in other spices the number of identified VOMs ranged from 49 (cumin) to 59 (cinnamon). The identified VOMs belong to several chemical groups including sesquiterpenoids (**66**), terpenoids (**48**), carbonyl compounds (**14**), and hydrocarbons (**12**). At a lower extent were identified eight esters, six volatile phenols, three furanic compounds, two higher alcohols, two nitrogen compounds and one sulfur compound (Figure 1A). Figure 1B shows the distribution of the total volatile fraction for each investigated spice. The sui generis character of spices and other foods is influenced by the composition, the presence of key aroma compounds, and concentrations which can determine the kind of aroma perceived [24]. The major VOMs chemical class in the current study (Figure 1A) is the class of terpenoids, which are widely produced in the plant kingdom [25], followed by sesquiterpenoids, and to a lower extent by carbonyl compounds. The predominant VOMs present in the top 10 investigated spices include \(\alpha\)-curcumene and dihydrojuneol in saffron; cinnaldehyde and copaene in cinnamon; cuminaldehyde and \(\gamma\)-terpinene in cumin; carryophyllene and 3-carene in black pepper; \(p\)-cymene and \(\gamma\)-terpinene in sweet paprika; and cuminaldehyde and linalool in curry. The detailed list of all VOMs identified in the analyzed spices and their respective obtained experimental data, including retention times, KIs, molecular formula (MF), chemical families, and relative peak area, is shown in Table 1.  
Figure 1: (**A**): Distribution of the identified chemical classes according to spice; and (**B**): Distribution of the total volatile fraction for each investigated spice. VP: Volatile phenols; SC: Sulfur compounds; NC: Nitrogen compounds; EST: Esters; TERP: terpenoids; A: Alcohols; SESQ: Sesquirepenoids; FC: Furanic compounds; CC: Carbonyl compounds; Hyd: Hydrocarbons.
# Visual Data and Pattern Analysis for Smart Education:
## 1 Introduction
### Research Contribution  
This research aims to improve the academic well-being of students by providing robust, secure, and tailored recommendations, leveraging advanced AI techniques. The key contributions of this study are outlined as follows:  
1. Enhanced Security Against Cyber Threats: this work introduces federated learning-based authentication combined with the Cycle-GAN algorithm to mitigate risks, such as malicious traffic and primary vulnerabilities, enhancing the security framework within the e-learning environment.
2. Improved Accuracy in Performance Predictions: We have implemented the MV-MOC algorithm for clustering weekly student engagement data using an intelligent software agent. This method integrates various student and instructor activities, significantly improving the precision of early performance predictions.
3. Boosted Prediction Reliability: The development of an ID2 QN-based early warning system aims to increase the accuracy of performance forecasts, thereby elevating the true positive rate of student evaluations. This has been complemented by creating a multi-disciplinary ontology graph to refine and expedite the recommendation process.
4. Enhanced Academic Support: The research introduces Att-CapsNet, an automated, personalised recommendation engine that extracts data from the ontology graph to formulate targeted academic advice. This system also tracks student progress over time, supporting an increase in overall academic performance rates.
# Studies of Fracture Damage Caused by the Proppant Embedment Phenomenon in Shale Rock
## Materials and Methods
### Methodology for Studying the Embedment Phenomenon
The test took into account the width reduction which can occur as a result of proppant crushing and proppant grains rearrangement within the fracture. The conditions of test no. 2 are presented in Figure 4:7 In test no. 2, cylindrical core plugs were replaced with cylindrical steel plugs. After 6 hours of exposure to the defined axial compressive stress, a maximum fracture width \(W_{fin}\) of 1.514 mm was obtained. The uncertainty of the estimated width of the fracture packed with proppant was determined on the basis of the accuracy of the LVDT fracture gauge +/- 0.001 mm. The uncertainty of the estimated total average depth of proppant embedding in the fracture faces was determined on the basis of the standard deviation from the average value. The parameters of the fracture effectively packed with the proppant are presented in Figure 4:8 \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Fracture**} & **No. of the** & **Surface** & **Average** & **Total** & & \\  & **Tested** & **Area** & **Measurement** & **Measurement** & & \\  & **Area** & **(mm\({}^{2}\))** & **Section Length** & **Sections** & **\(H_{e}\) (mm)** & **\(He_{e}\) (mm)** & **\(He_{e}\) (mm)** \\ \hline \multirow{2}{*}{Top} & **1** & 7.2188 & 2.8230 & 11.2919 & 0.0141 & \multirow{2}{*}{0.0170} \\  & **2** & 6.8904 & 2.6717 & 10.6866 & 0.0200 & & \\ \hline \multirow{2}{*}{Bottom} & **1** & 6.8251 & 2.6658 & 10.6632 & 0.0065 & \multirow{2}{*}{0.0084} \\  & **2** & 6.9765 & 2.6620 & 10.6481 & 0.0102 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Total average depth of proppant embedment in the fracture faces—Test no. 1. Figure 14: Parameters of the fracture effectively packed with the proppant for cylindrical steel plugs and shale rock. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Fracture Face**} & **No. of the** & **No. of the** & **\(W_{e}\) (mm)** & **\(PDW_{e}\) (\%)** & **\(PDW_{e}\) (\%)** & **\(PDW_{e}\) (\%)** \\  & **Tested Area** & **\(W_{e}\) (mm)** & **\(PDW_{e}\) (\%)** & **\(PDW_{e}\) (\%)** & **\(PDW_{e}\) (\%)** \\ \hline \hline \multirow{2}{*}{**Top**} & **1** & 1.9268 & 17.1 & \multirow{2}{*}{19.2} & \multirow{2}{*}{**17.1**} \\  & **2** & 2.2712 & 21.2 & & \\ \hline \multirow{2}{*}{**Bottom**} & **1** & 1.5250 & 14.3 & \multirow{2}{*}{14.9} & \multirow{2}{*}{**17.1**} \\  & **2** & 1.6647 & 15.6 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average percentage damage of the fracture surface—Test no.1. In addition, an attempt was made to simulate the effect of embedment on an effectively packed fracture with only one proppant layer. The maximum fracture width, corresponding to the average diameter of the tested proppant grains, amounting to 0.450 mm, was used for the calculation. It was assumed that the value of the average depth of proppant embedment \(Het_{t}\) and surface damage \(PDWe_{t}\), were equal to the values obtained in test no. 1. The results of this analysis are presented in Figure 15.