esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 2 DOF IMC Strategy for SRMs Based on ADO
### Stability Analysis and Parameter Tuning for the ADO  
To guarantee the stability of the proposed ADO, a discrete-time Lyapunov function is selected as:  
\[\mathbf{V}(k)=\frac{1}{2}\mathbf{e}(k)\mathbf{e}(k)^{T} \tag{27}\]  
According to the Lyapunov convergence criterion, if the following condition (28) is satisfied, the observed system is Lyapunov-stable.  
\[\mathbf{V}(k)\triangle\mathbf{V}(k)<0 \tag{28}\]  
where:  
\[\triangle\mathbf{V}(k)=\mathbf{V}(k+1)-\mathbf{V}(k)=\frac{1}{2}(\mathbf{e}(k+1)^{T}\mathbf{e}(k+ 1)-\mathbf{e}(k)^{T}\mathbf{e}(k)) \tag{29}\]  
Since \(\mathbf{V}(k)>0\), the \(\triangle\mathbf{V}(k)<0\) should be satisfied. Assuming that the observed disturbance is naturally continuous, with a bandwidth significantly less than the observation period, the change value \(\triangle\mathbf{e}(k)\) can be calculated by combining (22) and (23) with the adaptation law in (26).  
\[\triangle\mathbf{e}(k)=\mathbf{e}(k+1)-\mathbf{e}(k)=-\mu\mathbf{B}\mathbf{B}^{T}\mathbf{e}(k) \tag{30}\]  
Substituting (30) into (29), the following equation can be deduced:  
\[\triangle\mathbf{V}(k)=-\frac{1}{2}\mathbf{e}(k)^{T}\mu\mathbf{B}\mathbf{B}^{T}(2\mathbf{I}_{3 \times 3}-\mu\mathbf{B}\mathbf{B}^{T})\mathbf{e}(k) \tag{31}\]  
where \(\mathbf{I}_{3\times 3}\) is a unit matrix. As the \(\mu\) is a positive value, the \((2\mathbf{I}_{3\times 3}-\mu\mathbf{B}\mathbf{B}^{T})\) should be a positive definite matrix. Therefore, by combining (22), the range of the \(\mu\) can be obtained, as shown in (32).  
\[0<\mu<\frac{2L_{dc}^{2}+L_{ac}^{2}+\sqrt{2}L_{ac}L_{dc}}{T_{s}^{2}} \tag{32}\]  
As a consequence, the suggested observer's stability may be ensured when \(\mu\) matches the criterion in (32).
## 3 Results
### Fatty Acids Analysis  
Gas chromatography coupled with mass spectrometry (GC-MS) is considered the primary method for analyzing fatty acids in various types of plant samples [50]. Typically, fatty acids are not sufficiently volatile or stable to be directly analyzed by gas chromatography techniques, so they must be converted into fatty acid methyl esters (FAMEs) to improve their volatility and thermal stability [50].  
The scientific literature reports that long-chain polyunsaturated fatty acids (PUFAs) exhibit immunomodulatory effects [51]. Omega-3 (W-3) fatty acids have been shown to support immune system function by increasing the levels of protective antibodies such as IgA, IgG, and IgM, while reducing the production of IgE, which is involved in allergic reactions [52].  
Horseradish has a very fine oil, and the antioxidant activity of oil extracted from horseradish roots has been reported to be superior to both natural antioxidants, such as \(\alpha\)-tocopherol, and synthetic antioxidants, such as butylhydroxyanisole (BHA) and butylhydroxytoluene (BHT) [23]. Figure 8 illustrates the fatty acid profile identified in freeze-dried horseradish, determined by gas chromatography coupled with mass spectrometry.  
Figure 8: Fatty acid profile identified in freeze-dried horseradish by GC-MS.  
Figure 7: Chromatograms for vitamin C in the hydroalcoholic extract (**A**) and the aqueous extract (**B**).  
Following the chromatographic analysis of fatty acids from the freeze-dried horseradish, both saturated fatty acids (SFAs) and mono- (MUFAs) and polyunsaturated fatty acids (PUFAs) were identified, with the latter predominating. Linolenic acid was identified in the highest proportion (30.68%), being a derivative of \(\alpha\)-linolenic acid (ALA), which belongs to the omega-3 (n-3) fatty acid class. These essential fatty acids are known for their protective and beneficial role in allergic diseases [51]. According to the scientific literature, this fatty acid is present in the highest amount in both the roots and leaves of horseradish [52; 53; 54].  
The second most abundant compound was oleic acid (C18:1) with 22.09% and linoleic acid (C18:2 trans) with 18.92% of the total. The total content of saturated fatty acids (SFA) was lower (16.19%), the most representative being palmitic acid (C16:0), with a percentage of 13.38% of the total fatty acids identified.
## 5 Experiment Validation
### Image Extraction and CNN Design
#### 5.2.2 Pooling Layer  
As the number of computations increases exponentially with a fully connected layer, over-fitting problems may occur. A pooling layer is used to solve this problem. To adjust the image to an appropriate size and emphasize specific features, we selected a 3 \(\times\) 3 pooling layer and a 2 \(\times\) 2 pooling layer. After passing through the 2 \(\times\) 2 pooling layer, the output is reduced by 1/4. As a result of Max Pooling, neurons respond to the largest signals, therefore reducing noise, increasing speed, and improving image discrimination.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Layer (Type)** & **Output Shape** & **Parameter \({}^{*}\)** & **Activation Function** \\ \hline conv2d\_15 (Conv2D) & (None, 123, 123, 32) & 3488 & ReLU \\ max\_pooling2d\_15 (MaxPooling2D) & (None, 41, 41, 32) & 0 & - \\ conv2d\_16 (Conv2D) & (None, 38, 38, 60) & 30,780 & ReLU \\ max\_pooling2d\_16 (MaxPooling2D) & (None, 19, 19, 60) & 0 & - \\ flatten\_5 (Flatten) & (None, 21660) & 0 & - \\ dropout\_4 (Dropout) & (None, 21660) & 0 & - \\ dense\_10 (Dense) & (None, 128) & 2,772,608 & ReLU \\ dropout\_5 (Dropout) & (None, 128) & 0 & - \\ dense\_12 (Dense) & (None, 64) & 8256 & ReLU \\ dense\_10 (Dense) & (None, 2) & 130 & SoftMax \\ \hline \hline \end{tabular}  
* \({}^{*}\) Total parameters: 2,815,262/Trainable parameters: 2,815,262/Loss = CrossEntropy, Optimizer = Adam, Epoch = 10, Batch size = 15, Shuffle = True.  
\end{table}
Table 1: Layer, output shape, and parameters of the convolutional neural network.  
Figure 6: Convolutional Neural Network Structure.
## 2 Materials and Methods
### Processing of RGB Color Sensors
#### 2.2.3 Colorimetric Mapping of \(RGB\) Sensor Readouts  
Based on the measured spectral sensitivities of the specific RGB color sensor used in this work, a nonlinear matrix optimization procedure is proposed in order to minimize the mapping error when transforming from the sensor's \(RGB\) to \(XYZ\) tristimulus values. Figure 7 summarizes the optimization workflow. Starting from a training database of \(n\) different test light sources, CIE \(XYZ\) tristimulus values are calculated first by using:  
Figure 5: Optimization pipeline for correcting the measured spectral sensitivity (SS) model. The correction is performed on the color sensor’s sensitivity curves by introducing suitable wavelength-specific correction factors that minimize the differences between real (measured) and virtual (calculated from the SS model times correction factors) sensor responses. Figure 6: Corrected spectral sensitivity curves \(R^{\prime}(\lambda)\), \(G^{\prime}(\lambda)\), and \(B^{\prime}(\lambda)\) as a function of wavelength in comparison to the CIE \(2^{\circ}\) color matching functions \(\overline{x}(\lambda)\), \(\overline{y}(\lambda)\), and \(\overline{z}(\lambda)\). \[ \begin{split} X_{i}&=\int_{380}^{780}S_{\text{rel},i}( \lambda)\cdot\overline{x}(\lambda)\,d\lambda,\\ Y_{i}&=\int_{380}^{780}S_{\text{rel},i}(\lambda) \cdot\overline{y}(\lambda)\,d\lambda,\\ Z_{i}&=\int_{380}^{780}S_{\text{rel},i}(\lambda) \cdot\overline{z}(\lambda)\,d\lambda,\end{split}  \tag{3}\]  
where \(S_{\text{rel},i}\) denotes the relative SPD of the \(i\)th test light source. Next, the corresponding CIE 1976 uniform chromaticity scale coordinates \((u^{\prime}_{\text{CIE},i^{\prime}},v^{\prime}_{\text{CIE},i^{\prime}})\)[125] are calculated by applying the following set of equations:  
\[ \begin{split} u^{\prime}_{\text{CIE},i}&=\frac{4X_{ i}}{X_{i}+15Y_{i}+3Z_{i}},\\ v^{\prime}_{\text{CIE},i}&=\frac{9Y_{i}}{X_{i}+15Y _{i}+3Z_{i}}.\end{split}  \tag{4}\]  
Figure 7: Optimization workflow used to find an optimal transformation matrix to map from the color sensor’s \(RGB\) output to CIE \(XYZ\) tristimulus values. A similar processing can be applied to the virtual channel responses of the color sensor. Thus, the sensor's readouts \(R_{i}G_{i}B_{i}\) for each test light source \(i\) of the training set are given by:  
\[R_{i} =\int_{380}^{780}S_{\text{rel},j}(\lambda)\cdot R^{\prime}(\lambda)\,d\lambda, \tag{5}\] \[G_{i} =\int_{380}^{780}S_{\text{rel},i}(\lambda)\cdot G^{\prime}(\lambda) \,d\lambda,\] \[B_{i} =\int_{380}^{780}S_{\text{rel},i}(\lambda)\cdot B^{\prime}(\lambda )\,d\lambda.\]  
These readout values are then converted to estimated \(X_{\text{SS},j}Y_{\text{SS},j}Z_{\text{SS},j}\) tristimulus values by applying a matrix transformation of the form:  
\[ \begin{pmatrix}X_{\text{SS},i}\\ Y_{\text{SS},i}\\ Z_{\text{SS},i}\end{pmatrix}  = \begin{pmatrix}m_{1,1}&m_{1,2}&...&m_{1,n}\\ m_{2,1}&m_{2,2}&...&m_{2,n}\\ m_{3,1}&m_{3,2}&...&m_{3,n}\end{pmatrix} \cdot \begin{pmatrix}R_{i}\\ G_{i}\\ B_{i}\\...\end{pmatrix} .
# Independent Random Recurrent Neural Networks for Infrared Spatial Point Targets Classification
## 4 Experiments and Discussion
### Classification Experiment of Radiation Intensity Sequences
#### 4.2.1 Classification Experiment

Table 4: Classification accuracy of four algorithms when \(\text{t}_{\text{beg}}=200\) s. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Observing Time (s)**} & \multicolumn{5}{c}{**Accuracy**} \\ \cline{2-6}  & **RNNs** & **IndRNN** & **RRNN** & **IRRNN** & **B-IRRNN** \\ \hline \(8\) & 0.6375 & 0.8081 & 0.7729 & 0.8856 & 0.9124 \\ \(16\) & 0.7449 & 0.8424 & 0.8302 & 0.8987 & 0.9176 \\ \(24\) & 0.7892 & 0.8840 & 0.8743 & 0.9101 & 0.9315 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification accuracy of four algorithms when \(\text{t}_{\text{beg}}=150\) s. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Observing Time (s)**} & \multicolumn{5}{c}{**Accuracy**} \\ \cline{2-6}  & **RNNs** & **IndRNN** & **RRNN** & **IRRNN** & **B-IRRNN** \\ \hline \(8\) & 0.6416 & 0.7953 & 0.7864 & 0.8751 & 0.8939 \\ \(16\) & 0.7568 & 0.8413 & 0.8395 & 0.8903 & 0.9120 \\ \(24\) & 0.8059 & 0.8725 & 0.8874 & 0.9026 & 0.9234 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification accuracy of four algorithms when \(\text{t}_{\text{beg}}=250\) s.  
(3) The classification performance of the IRRNN algorithm proposed in this paper is more prominent than the independent IndRNN algorithm and RRNN algorithm. Combining the advantages of both of them, the performance of the classification algorithm is significantly enhanced. (4) The B-IRRNN model obtained the best classification performance at all observation times, and its classification accuracy was higher than that of the unidirectional IRRNN model. Because the B-IRRNN classification model has two hidden layers of forward propagation and backward propagation, it can simultaneously use past and future sequence information to help the classification decision at current time. It has more advantages than the unidirectional IRRNN model in time series classification.
# Key Barriers to Personalized Learning in Times of Artificial Intelligence: A Literature Review
## 3 Results
### Methodological Strategies in PL Studies  
The analysis of publications on PL does not show a clear preference for any of the three research approaches. The trend shows a slight inclination towards mixed methods (n = 26 and f = 38.2%), followed by quantitative (n = 22 and f = 32.4%) and qualitative methods (n = 20 and f = 29.4%).  
Mixed methods combine elements of both qualitative and quantitative research, allowing for a more comprehensive and nuanced understanding of the phenomenon under study. This approach can be particularly useful when quantitative data alone is insufficient to understand the full context of a problem [54]. For example, [PERSON] & [PERSON] [55] conducted a study in which they designed an autonomous robot tutor that delivers personalized lesson content and interacts with preschool children. Their research focused on the effectiveness of the social robot for learning, including different factors such as behavior, the unique characteristics of children when interacting with machines, and the learning impact.  
On the other hand, quantitative methods, such as experimental and quasi-experimental designs, are useful for evaluating strategies to achieve PL by comparing experimental and control groups, facilitating the collection of numerical data and the demonstration of causal relationships [54]. An example is the study by [PERSON] et al. [53], which treated human learning as a sensory input signal and tested their ERUDITE platform, which provided personalized feedback to enhance the educational experience. Evaluating 15 participants, they demonstrated that by using brain signals to adapt to the learning environment, academic performance increased by an average of 26%.  
Finally, qualitative methods focus on understanding the meaning and interpretation of social phenomena from the participants' perspective. These methods are useful for exploring complex and contextual phenomena and for developing theories or hypotheses [54]. An example is the study by [PERSON] et al. [56], which sought to understand how teachers adopt NAVIGO technology, an adaptive personalized literacy game used regularly in primary school classrooms.  
Figure 2: Word Cloud of Key Terms.
# GNSS Accuracy Under White Gaussian Noise Jamming +
## 3 GNSS Jamming Experiment  
The equations provided in the previous Section were compared to actual measurements. These measurements were obtained with a software-defined receiver. As over-the-air jamming in the GNSS frequency bands is strictly prohibited, a high-fidelity GNSS receiver was used to generate the satellite signals along with a GNSS jammer signal.
# Path Planning of Intelligent Mobile Robots with an Improved RRT Algorithm
## 1 Introduction
[18] proposed a dynamic path planning algorithm for unmanned aerial vehicles that integrates improved Bi-RRT and DWA. By setting heuristic functions, dynamic step sizes, and safety distances, the Bi-RRT algorithm is enhanced, enabling precise tracking of the global optimal path planning and efficient local obstacle avoidance. [PERSON] et al. [19] introduced an improved RRT algorithm with a restricted sampling area (RSA-RRT). For narrow areas, it proposes a fixed angle sampling strategy, achieving shorter paths in less time and effectively balancing path quality and planning speed. Based on the improvements to the RRT algorithm and existing research results, this article first proposes a variable steering angle strategy on the traditional RRT algorithm to prevent the random search tree from experiencing reverse growth, thereby improving search efficiency. Secondly, Kalman filtering is incorporated into the strategy for redundant point removal, which deletes redundant points along the path while reducing the number of effective points on the path to some extent, thereby forming a new path, improving search efficiency, and reducing path length. Finally, second-order Bezier curves are used to smooth the waypoints along the path.
## 3 Results and Discussion
### Two-Wavelength Coupling Experiment
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & \(\tau_{w}\) **(s)** & \(\tau_{\varepsilon}\) **(s)** & \(\eta_{s}\) **(\%)** & \(S\) **(cm/J)** & \(M/\#\) & \(\Delta n_{s}\) **(\(\times\)10–5)** & \(\sigma_{ph}\) **(\(\times\)10–12 cm/\(\Omega\)W)** \\ \hline Zn1 & 603.9 & 782.4 & 50.2 & 0.29 & 0.92 & 4.60 & 0.41 \\ Zn3 & 528.1 & 1108.6 & 57.3 & 0.36 & 1.59 & 4.92 & 0.47 \\ Zn5 & 483.5 & 1390.9 & 62.8 & 0.41 & 2.28 & 5.15 & 0.51 \\ Zn7 & 196.4 & 2358.2 & 66.7 & 1.04 & 9.81 & 5.30 & 1.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experiment results of holographic storage properties of Zn:Cu:Fe:LiNbO\({}_{3}\) crystal. higher than the 0.92 recorded for sample Zn1. Additionally, the saturation diffraction efficiency (\(\eta_{s}\)) of Zn7 reached 66.7%, representing a 16.5% improvement over the 50.2% achieved by Zn1. In summary, the holographic storage performance of the crystal improved significantly with higher Zn\({}^{2+}\) ion doping concentrations. Specifically, when the Zn\({}^{2+}\) ion doping concentration reached 7 mol%, the Zn:Cu:Fe:LiNbO\({}_{3}\) crystal demonstrated exceptional holographic storage performance, making it a highly promising material for such applications.
###### Abstract  
As shield tunnels increase, managing shield muck strains construction and the environment. To mitigate this problem, shield muck replaced bentonite in silty clay to improve synchronous grouting slurry. Initially, the physical attributes and microstructural composition of shield muck were obtained, alongside an analysis of the effects of the muck content, particle size, and general influencing factors on the slurry properties through standardized tests and regression models. Subsequently, leveraging three-dimensional response surface methodology, admixture interactions and multiple factor impacts on the slurry were explored. Finally, utilizing the SQP optimization technique, an optimal slurry blend ratio tailored for actual project needs was derived for improved muck slurry. The findings reveal with the decreasing bleeding rates as the muck content rises, the particle size diminishes. An inverse relationship exists between the muck content and slurry fluidity. At soil-binder ratios below 0.6, a decrease in the soil-binder ratio intensifies the influence of the water-binder ratio on the slurry density, bleeding rate, and setting time. The fly flash-cement ratio inversely correlates with the slurry bleeding rate, while the ratio greater than 0.6 is positively correlated. For muck particle sizes under 0.2 mm, the fly flash-cement ratio inversely impacts the density, while over 0.2 mm, it correlates positively. The optimal proportion for silty clay stratum synchronous grouting slurry, substituting muck for bentonite, includes a water-binder ratio of 0.559, binder-sand ratio of 0.684, fly flash-cement ratio of 2.080, soil-binder ratio of 0.253, particle size under 0.075 mm, and water-reducing admixture of 0.06.  
shield tunnel; slurry pressure balanced shield machine; synchronous grouting; recycling muck; silty clay; regression analysis; response surface +
Footnote †: journal: Applied sciences
# Research on the Impact of BDS-2/3 Receiver ISB on LEO Satellite POD
## 4 Results and Analysis
### GPS-Based Orbit  
Figure 2 shows the GPS-based POD residuals of code pseudo-orange and the carrier phase measurements. The residual RMS value of the code pseudo-orange measurements is about 0.76 m, and the residual RMS value of the carrier phase measurement is about 0.96 cm.  
Figure 3 shows the RMS differences in overlapping consistency of the GPS-based orbits, and Table 2 shows the relevant statistical results. The overlap consistency 3D-RMS average is 1.28 cm, and the standard deviation is less than 0.3 cm. The 3D-RMS maximum value of the overlap consistency is 1.9 cm, and the minimum value of that is 0.8 cm. The radial overlap consistency average is 0.56 cm, and the standard deviation is less than 0.2 cm. The maximum and minimum values of that  
It can be seen that the 3D orbital overlap consistency of GPS-based POD is less than 2 cm, and the radial overlap consistency is less than 1 cm. Therefore, the GPS-based orbit solutions could be as a precision product to evaluate BDS-based orbit solutions.
## 4 Discussion  
### Overall Performance  
In this study, we developed a CNN model to determine the presence of pre-collapse ONFH. The AUCs of our models, as evaluated on the test dataset, were 0.848 and 0.939 for the right and left hip joints, respectively, based on anteroposterior pelvic radiographs classified into \"ONFH\" and \"normal\". Considering that an AUC \(\geq\) 0.9 and 0.9 \(>\) AUC \(\geq\) 0.8 are generally outstanding and excellent, respectively, our model for determining the presence of ONFH based on anteroposterior pelvic radiographs seems to be excellent to outstanding [21].  
In addition, whereas the accuracy of the two physicians specializing in musculoskeletal disorders in determining the presence of ONFH was approximately 76-80%, that of our model was approximately 90%. We believe that our model would be useful for clinicians in diagnosing pre-collapse ONFH using pelvic radiographs but not MRI or computed tomography (CT) scans. In clinical practice, because of the high cost and limited accessibility of MRI, clinicians frequently diagnose hip joint pathologies using physical examination, and pelvic or hip radiography. However, because of its low diagnostic accuracy, the exact diagnosis of ONFH is often limited in clinical scenarios [2,4,5]. Although the accuracy of our CNN model was not comparable with that of MRI, we believe that a CNN model trained using anteroposterior pelvic radiographs can be used as a supplementary tool for clinicians to determine the presence of pre-collapse ONFH.  
A deep learning algorithm consists of a multilayer perceptron with multiple hidden layers and a feedforward neural network, which allows it to recognize detailed characteristics of input data [6,7,12]. A CNN is a deep-learning algorithm that receives image data as input and transforms these data repeatedly using convolution and pooling operations [6,7,12]. Through these processes, important features or patterns of input data can be extracted and differentiated [6,7,12]. We believe that our developed CNN algorithm can recognize valuable features or patterns of pelvic radiographic images and can extract specific characteristics, such as sclerosis or cystic changes, from ONFH and normal images.