esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Comparison of Theoretical Simulation and Performance Tests
### Comparative Analysis of Theoretical Simulations and Performance Experiments  
Usually, in fluid simulation, the selection of working conditions near the rated flow rate for simulation can improve the calculation accuracy and stability of the results. The common practice is to select a range of rated flow, which is usually simulated between 0.6 and 1.4 times the standard condition. Figure 6 shows the comparison between the numerical simulation's external characteristics and the experimental external characteristics curve. Since the model is a two-stage model and the experimental model is a six-stage model, the two-stage data obtained from the simulation needs to be converted to a six-stage model. Under standard working conditions, the experimental test head and efficiency are basically consistent with the results of the numerical simulation. The simulated head is slightly lower than the experimental head, and the maximum error does not exceed 3%. At the same time, the simulated efficiency is slightly higher than the experimental efficiency, and the maximum error does not exceed 3.4%. This shows that the simulation results can completely replace the experimental test results and can predict the actual performance of the submersible well pump, which provides a basis for further simulation. The main reasons for the errors between simulation and experimental tests are that one is influenced  
Figure 5: Pump performance test schematic diagram.  
by the configuration of the computer and amount of meshing, and the other is influenced by the simplification of the simulation model.
# Quantitative Analysis of Drag Reduction Methods for Blunt Shaped Automobiles
## 2 Literature Review
### Aerodynamic Drag
#### 2.1.2 Numerical Models  
Numerical (CFD) models intended to predict flow in a computational domain with the combination of Navier-Stokes equations and turbulence prediction techniques. Nowadays, three main methods used: large eddy simulation (LES), detached eddy simulation (DES), and Reynolds Averaged Navier-Stokes (RANS) equations. For drag related problems, on one geometry, LES can use [16] to compare results with PIV [17]. For optimization where numerous geometries are examined, steady state RANS simulations are preferred due to low computational demands. However, C\({}_{\text{D}}\) in most of the cases has to be calculated over time, in attribute to vortex fluctuations, as [PERSON] et al. [18] shows. Steady state modelling can only be used when deviations are in an acceptable range. Utilizing the advantage of both methods, DES can be applied. The purpose of this method is that it only calculates with LES where vortex shedding occurs, while the rest is calculated with RANS. With DES, vortex shedding can be examined with relative less computational time [19]. RANS method is simple, yet in valid cases, it could show, as precise values as attained with LES [20; 21].  
PIV and CFD methods should be bounded strongly together to compare flows. Simulations carried out by the RANS method should be compared with PIV to ensure accurate predictions.
# New Methods for the Comprehensive Analysis of Bioactive Compounds in _Camabis sativa_ L. (hemp)
## 2 Results and Discussion
### Quantitative Analysis of Non-Psychaoticy Cannabinoids in Hemp by HPLC-UV/DAD  
Quantitative data related to the content of non-psychoactive cannabinoids in hemp infloresences determined by means of the HPLC-UV/DAD method are shown in Table 2. Since all the samples analysed are fibre-type hemp varieties, it is not surprising that the most abundant cannabinoids were represented by CBDA and CBD [9]. In particular, samples C1, C3, and C6 showed the typical cannabinoid profiles of the fresh plant material, with CBDA as the main compound, followed by CBD. On the other hand, samples C2, C4, and C5 showed the opposite behaviour: as a matter of fact, CBD represented the most abundant cannabinoid in these samples, followed by CBDA. This profile may be attributed either to an aged plant material or to a non-optimal drying process applied to the infloresences or to unsuitable storage conditions that may have led to a partial conversion of the parent cannabinoic acid into its neutral counterpart, via a decarboxylation process, which naturally occurs with time under the action of heat and light [9].  
The same trend was observed also for the other two major non-psychoactive cannabinoids, namely CBGA and CBG, though their content was about 10 times lower in comparison to CBDA and CBD and, in some cases, not quantifiable. Nevertheless, the amount of non-psychoactive cannabinoids determined in the analysed samples is in agreement with what is described in the literature for the content of cannabinoids in hemp female infloresences [9].
## 1 Introduction
In light of this backdrop, the current work provides the results of training using transfer learning in architectures that have just been investigated for agricultural applications, as well as their implementation on affordable hardware like the android mobile and Raspberry Pi 4 microcomputer. In order to obtain findings that help in the classification of chickpeas, one of the objectives of this research is to incorporate CNN models in a low-cost, low-power device that can process information in real time. Therefore, four common Canadian chickpea varieties were utilised and an attempt was made to identify them utilising computer vision and machine-learning-based methodologies in order to automate the process of chickpea classification. The work's goal is broken down into two subcategories. The first stage involves creating the classification model using computer vision and machine-learning-based approaches. The deployment phase, which involves deploying the trained machine-learning model on Android mobile and Raspberry Pi 4 devices, and evaluating its performance, takes place in the second stage.
## 1 Introduction
introducing new methods for real-time data integration and analysis, this study aims to provide valuable information for industry, software development, and data science, supporting the advancement of Industry 4.0. This study aims to contribute to system architecture, dynamic data acquisition, and knowledge modeling by addressing the following key research questions. How can a system architecture be designed to efficiently support dynamic data collection for knowledge modeling in industrial environments? This question focuses on developing a scalable, flexible, and industry-compatible framework for real-time data integration. What methods and tools enable the creation and continuous updating of the knowledge model for multiple users within the proposed architecture? The focus here is on designing user-centric and adaptive knowledge modeling techniques. How can semantic conflicts between heterogeneous data sources be resolved within the architecture? Establishing data harmonization techniques ensures data integrity and usability. This research is based on a comprehensive review of the literature that analyzes current system architectures. It then explores innovative approaches to data integration and knowledge modeling, leading to the development of a prototype based on a KAVA model. Knowledge-Assisted Visual Analytics (KAVA) [15; 16] combines visual analytics with human expertise, improving data interpretation and real-time decision-making [17; 18]. An example of a KAVA system is gaitXplorer, which classifies gait patterns in patients with cerebral palsy using machine learning [19]. By answering these research questions, this study makes a significant contribution to both research and industrial practice, offering innovative solutions for real-time data processing, the integration of heterogeneous data sources, and the improvement of knowledge modeling. This work is motivated by a central question in smart manufacturing: how to design a system that supports real-time, high-frequency data acquisition across legacy and modern sources, while enabling adaptive knowledge modeling in production environments. These challenges are formally characterized in Section 4, which defines the technical and operational boundaries for system design.
## 5 Proposed Model Architecture
### Proposed U-IntentCapsNet
#### 5.2.1 Embedding Layer  
The training data is of the form \(\left\langle x,\ y\right\rangle\), where \(x=\) query and \(y=\) intent label. The intent \(y\) belongs to a set of \(K\) intents, \(y\in\{y_{1},y_{2},\ldots,y_{k}\}\). Each query \(x_{i}\) is tokenized to represent a sequence \(x_{i}=(w_{1},\ w_{2},\ \ldots,w_{T})\), where \(T\) is the total number of words. Each word \(w_{i}\) is initially represented by a vector of dimension \(D_{W}\). An appropriate word embedding that is context-free or contextualized, pre-trained or trained from scratch, can be exploited based on its comparative performance. In the proposed model, the word embeddings are learnt from the dataset.
# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 3 Proposed Method
### General Architecture
#### 3.1.2 Step 2: Robust Block Selection  
A critical aspect of any watermarking scheme is choosing regions where embedded data will remain intact after typical processing or attacks. This step ensures robustness by selecting blocks with favorable perceptual and structural properties, as determined by the perceptual score map from Section 3.1.1. Step 1.  
The goal is to prioritize blocks with high tolerance to distortion and low perceptual visibility. Blocks with complex texture or low saliency tend to retain embedded information after compression or filtering. Embedding data in these blocks minimizes chances of detection by the human visual system.  
This aligns with observations in recent surveys on watermarking robustness [3], which show that selecting textured or noisy regions improves survival under MPEG compression, resizing, and Gaussian noise. To perform selection, we apply a hard threshold \(\tau\) on the perceptual score map \(P_{i,j}\). Blocks whose score exceeds this threshold are considered robust:  
\[B_{sel}=\left\{B_{i,j}\ \middle|\ P_{i,j}\ >\ \tau\right\} \tag{3}\]  
where \(B_{sel}\) is the set of selected \(8\times 8\) blocks for watermark embedding, \(P_{i,j}\) is the perceptual score block \((i,j)\) computed in Section 3.1.1. Step 1, and \(\tau\) is the adaptive threshold. To maintain flexibility across different video contents, we implement an adaptive version of the threshold \(\tau\) based on a percentile criterion:  
\[\tau=\mu_{P}+\lambda\cdot\sigma_{P}\]  
where \(\mu_{P}\) denotes the mean of all \(P_{i,j}\), \(\sigma_{P}\) is the standard deviation of all \(P_{i,j}\), and \(\lambda\) is a scalar hyperparameter. This strategy allows the number of selected blocks to dynamically adjust depending on the video's perceptual complexity. To ensure that sufficient capacity is maintained for watermark embedding, the number of selected blocks must meet a minimum threshold \(N_{min}\). If not enough blocks satisfy the condition, \(\lambda\) is iteratively reduced until the following:  
\[|B_{sel}|\geq N_{min}\]  
This dual-constraint system (quality and quantity) ensures that the embedding is both robust and feasible.
## 7 Conclusion and Future Work  
There are many security issues including false data injection attacks in WSNs. SEF [9] is the first solution that can alleviate the impacts of the attacks. While a sensing report is being forwarded toward the base station, the report is verified by the forwarding nodes. PSM [12] can enhance the detection power of SEF. Every control message stores the information on the filtering keys of the nodes it traveled on, when paths are established by flooding. A sensor node has an evaluation function to choose the most secure path based on the information.  
In this paper, we proposed a path renewal method to provide WSNs with load balancing. A network is represented as a digraph and a communication traffic model for the network is proposed. Base on the model, an evaluation function to choose a new super-node is defined. The effectiveness of the propose method is shown with the simulation results. As future works, some AI algorithms will be applied in order to find further optimal solutions.
# Narrowband Internet of Things (NB-IoT):
## 2 Narrowband-IoT Standard and Releases
### Release 15 Enhancements
#### 2.3.4 Enhanced User Equipment Measurements  
Like in legacy LTE systems, UE measurements are critical since the corresponding reporting is mainly used to characterize the reference signal of a given bandwidth.  
In Release 15, UE measurements are improved in a way that only NSSS additionally to NRS is defined for radio resource management measurement enhancement. This means that NRS is determined by the resource elements that carry NSSS in the NSSS occasions that the UE measures, through which the cell search and initial cell acquisition are improved.
# Cross-Subject Classification of Effectiveness in Performing Cognitive Tasks Using Resting-State EEG
## 4 Discussion
In this study, a group statistical comparison did not show any between-group difference in resting-state EEG. Despite the absence of significant group differences in the PSD of resting-state EEG in this study, SHAP values interpret the significant impact of features of the light GLM results of cross-subject classification to lie in the frontal midline theta band. The frontal midline theta rhythm is usually associated with executive control functions [31] and complex cognitive activity [32]. Event-related theta rhythm power showed a positive association with polygenic scores of educational attainments in a large twin/family sample (\(n=4893\)) [33]. SHAP value analysis estimated almost all central frontal regions in the beta3 band (20-24 Hz) and to a lesser extent in the other beta ranges as very important features for classification. Similarly to the frontal midline theta, the beta rhythm in the frontal areas is closely related to the processes of executive control and working memory [34; 35]. A study by [PERSON] et al. 2020 [20] also showed that the beta band power (22-29 Hz) of the resting-state EEG correlated with the strength of frontoparietal connectivity and subsequent behavioral performance. However, we cannot claim that the EEG patterns revealed by lightGBM and estimated by SHAP analysis specifically reflect mathematical education. Rather, we would like to draw attention to the interpretive methods of machine learning models for studying the cognitive functions of the brain, as well as to the possibility and prospects of inter-subject EEG classification. The small sample size should be noted as a limitation of the current study. The sample size was probably sufficient to test the performance of the classifier but not enough to make reliable psychophysiological statements.
## 3 MSA-LMS Adaptive Filter
### MSA-LMS Adaptive Filter Implementation and Analysis
#### 3.2.4 Stage 3  
The error signal \(e(k)_{s2}\) is an unknown signal and cannot be measured. The frequency \(f_{3}\) of the error signal \(e(k)_{s2}\) is estimated from the number of times \(C\) the signal passes through the \(x\)-axis, as sensed by the zero-crossing detector and indicated by the counter. Using the output of the sine wave generator at the estimated frequency \(f_{3}\), the function \(u_{f3}\) between 3 and 3.9999 s is expressed as follows:  
\[u_{f3}=1/3\cdot\sin(2\cdot\pi\cdot f_{3}\cdot t_{2}) \tag{17}\]  
The signal is multiplied by \(1/3\) to avoid the amplitude of the input signal from increasing as the filter coefficient is updated. Thus, in Stage 3, the following signals appear additionally:  
\[\begin{split} u_{f1}&=1/3\cdot\sin(2\cdot\pi\cdot f _{1}\cdot t_{2})\\ u_{f2}&=1/3\cdot\sin(2\cdot\pi\cdot f_{2}\cdot t_{2}) \end{split} \tag{18}\]  
Therefore, the filter output of Stage 3 appears as follows,  
\[\begin{split} u_{f1}&=1/3\cdot\sin(2\cdot\pi\cdot f _{1}\cdot t_{2})\\ u_{f2}&=1/3\cdot\sin(2\cdot\pi\cdot f_{2}\cdot t_{2}) \\ u_{f3}&=1/3\cdot\sin(2\cdot\pi\cdot f_{3}\cdot t_{2}) \end{split} \tag{19}\]  
The total filter output signal \(y_{3}\) is shown as  
\[y_{3}=u_{f3}\cdot w_{3}^{T}=u_{f3}{}^{T}\cdot w_{3} \tag{20}\]  
Since there are three input signals in Stage 3, the total output signal of the filter \(y(k)_{3}\) appears as follows:  
\[\begin{split} y(k)_{3}&=y_{1}+y_{2}+y_{3}\\ &=1/3\cdot\sin(2\cdot\pi\cdot f_{1}\cdot t_{2})\cdot w_{1}^{T}+1 /3\cdot\sin(2\cdot\pi\cdot f_{2}\cdot t_{2})\cdot w_{2}^{T}\\ &\qquad+1/3\cdot\sin(2\cdot\pi\cdot f_{3}\cdot t_{2})\cdot w_{3 }^{T}\end{split} \tag{21}\]  
In Stage 2, the error signal \(e(k)_{s2}\) is \(d(k)\) of Stage 3. The filter output \(y(k)_{3}\) and error signal \(e(k)_{s3}\) are expressed as follows:  
\[e(k)_{s3}=e(k)_{s2}-y(k)_{3} \tag{22}\]  
The \(e(k-1)_{s3}\) signal is applied one step before the error signal \(e(k)_{s3}\) to the MSA-LMS algorithm and is updated repeatedly.
# Review: A Survey on Objective Evaluation of Image Sharpness
## 2 Evaluation Methods and Analysis
### Spectral Domain-Based Methods
#### 2.2.1 Fourier Transform-Based Methods  
The physical meaning of the Fourier transform is to convert the gray distribution function of an image into a frequency distribution function, while the inverse transform is to convert the frequency distribution function of an image into a gray distribution function [56]. Fast Fourier transform (FFT), discrete Fourier transform (DFT) and discrete cosine transform (DCT) [57] are common forms based on Fourier transform.  
[PERSON] et al. [58] utilized the Fourier transform spectrum to simulate the uniform blurring of Gaussian blurred images and fixed the threshold of high-frequency components for image sharpness assessment. In a related study, [PERSON] et al. [59] presented a new image sharpness measure that used Discrete Cosine Transform (DCT) coefficient-based features for generating the model of image sharpness assessment. [PERSON] et al. [60] presented a novel DCT-based Quality Degradation Metric, called DCT-QM, which was based on the probability summation theory. [PERSON] et al. [61] also proposed a visual quality assessment method that characterized local image features and various distortion types. The visual quality perception characteristics of HVS for local image features and various distortion types are characterized by adopting Structural Contrast Index (SCI) and DCT blocks. [PERSON] et al. [62] proposed a no-reference image quality assessment method based on the Discrete Fourier transform (DFT), calculated the image block-based DFT and averaged it at the block level and then combined them to obtain a clear metric for estimating the overall image perception quality.  
The Fourier transform, as one of the most basic time-frequency transforms, can convert the image from the spatial domain to the spectral domain, synthesizing the multi-scale features but also increasing the computational effort.
## 1 Introduction
However, these correlation approaches are not possible when the subset size is large compared to the field of view (i.e., when using low-resolution cameras), such as our ultra-high-speed camera (which has a resolution of \(250\times 400\) pixels[2]) or high-resolution cameras, but with comparatively large subset sizes, situations typically arising in the study of dynamic problems [3]. To resolve the displacement discontinuities on the interface associated with the propagation of dynamic shear ruptures, the studies in [1; 2; 3] used the commercial DIC software Vic-2D (Correlated Solutions, Inc.) to separately correlate the domains above and below the interface. While standard local DIC methods calculate displacements up to half a subset away from the interface, the \"fill boundary\" algorithm of Vic-2D employed in [1; 2; 3] uses affine transformation functions to extrapolate the displacements from the center of the subset up to the interface. This enables the use of large subsets in each side of the interface, which are essential to overcoming the noise associated with ultra-high-speed photography and the analyzed rapid ruptures. An important limitation of this approach, however, is that the extrapolated displacements are less accurate than those obtained directly by the actual correlation, especially at pixels near the interface, which have the largest distance from the subset center. This leads to errors in the strains and stresses near the interface, which are obtained from the spatial derivatives of the displacement fields. In addition, the non-coupled correlation of the domains above and below the interface results in non-physical discontinuities in the tractions across the interface. This study proposes a simple and fast method to supplement the DIC solution with a post-processing algorithm that enforces the continuity of normal and shear tractions across the interface. The algorithm uses a constrained inversion to construct local 2-D polynomials that satisfy traction continuity conditions at the interface while matching the displacements of pixels closer to the center of the subset, where the DIC solution is most accurate. The polynomials are then used to calculate all stress components near the interface. In Section 2, we describe the experimental setup, including the laboratory experiment, ultra-high-speed diagnostics, the digital image correlation approach, and the post-processing procedure to turn the displacement fields into strain and stress fields. In Section 3, we present the method to enforce traction continuity along the interface and study the effects of the parameters involved in this method on the full-field stresses, displacements and particle velocities. Implications for the analysis of friction using the stress fields produced by this approach and conclusions are given in Sections 4 and 5, respectively.
# Three-Dimensional Real-Scene-Enhanced GNSS/Intelligent Vision Surface Deformation Monitoring System
## 4 Data Processing
### Video Data Processing
#### 4.1.3 Edge Detection
A new bounding box is created by merging the two, with its upper-left corner coordinates set to the minimum values of the original boxes' coordinates. The width and height are the sum of the original boxes' dimensions. Cracks are then labeled (Figure 6f). Finally, the width-to-height ratio of the new bounding box is calculated. A ratio greater than 1 indicates a transverse crack, while a ratio less than or equal to 1 indicates a longitudinal crack. By analyzing row and column projections, the distribution of targets in the image can be quickly determined along the horizontal and vertical directions, as shown in the row projection plot (Figure 7a) and the column projection plot (Figure 7b).