esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### Dynamic Graph Connectivity  
#### 2.2.1 Spanning Forests and Euler Tour Trees  
Like other dynamic graph connectivity data structures [19; 21], HDT maintains a spanning forest \(F\) of the given graph throughout the lifetime of this graph. Edges of \(F\) are called _tree edges_; all other edges are _non-tree edges_. Each tree \(T\) in \(F\) is represented using a _Euler tour tree_ (_ET tree_) [19], a balanced binary tree over a Euler tour of \(T\). A Euler tour of an \(n\)-node tree has \(2n-1\) nodes, and a single node of the tree may appear multiple times in the tour. ET trees support the following operations in logarithmic time per operation: determining the size of the tree containing a given node, testing if two nodes are in the same tree, linking two trees with an edge, and deleting an edge from a tree.
## 4 Results and Discussion
### Performance Evaluation  
There are two types of classifiers considered in this paper-those based on four traditional machine learning models and those based on adaptive deep neural networks. As the training and test samples of the classifiers are the same, the interference of the data samples on the model results is effectively eliminated and the confidence of the comparison is improved.  
80% of the data was used to train the proposed ADL model, while 80% of the remaining 20% data is used to train the classifier and 20% for testing. To verify the effectiveness of the ADL algorithm, we compare its performance with that of a traditional machine learning model for feature extraction. The experimental procedure uses the KNN, the DT, the NB algorithm and the BPNN to train the classifier. The raw data was passed through an adaptive deep neural network to obtain data features, which improved the classification accuracy of the classifier overall and also reduced the detection time of the network for abnormal data.
# Deep Learning-Based Context-Aware Recommender System Considering Contextual Features
## 3 Proposed Model
### Learning Context Using Autoencoder  
In most cases, in a recommender system, collaborative filtering is classified into user-based CF and item-based CF. After measuring each user's (item's) similarity, user (item) correlations are assessed to make a proper recommendation. In prior research on recommender systems, relatively limited focus was placed on the contextual feature. Additionally, the contextual information applied to recent recommender systems mainly concerns time and weather. In this paper, we aim to reflect contextual features and much more diverse information by utilizing autoencoder. Within a recommender system, an autoencoder is mainly used to reduce the dimension of input data or extract the characteristics of input data. Focus is placed on the latter in the autoencoder used in the proposed model. Figure 1 illustrates a general autoencoder model in which context is used as an input data.  
In Figure 1, according to the left input, the contextual dimension is composed of vector \(v_{c_{n}}\), where \(n\) is the number of contextual dimensions. The more layers that are present, the higher the accuracy. However, the presence of more layers does not always result in better performance; experimentation is required to set the proper number of layers. The formulas used to train autoencoder and minimize errors are described in the next sub-section.  
\[\underset{\theta}{min}\sum_{v\in V}\left\|X-X^{\prime}\right\|^{2} \tag{1}\]  
X indicates input data. Input data X is passed through AE and comes out as the output \(X^{\prime}\). Formula (1) indicates learning while reducing errors between X and \(X^{\prime}\). \(\theta=\left\{w,b\right\}\) indicates the model parameters. \(k\)th layer output \(x^{k}\) can be formulated as follows.  
\[x^{k}=\sigma\left(w^{k}x^{k-1}+b^{k}\right) \tag{2}\]  
\(w^{k}\) represents weight and \(b^{k}\) represents bias. \(\sigma(x)\) is the activation function and we applied Exponential Linear Unit (ELU). ELU is the solution for dying ReLU problems where 0 is outputted when it has a negative value.  
\[\sigma(x)=\left\{\begin{array}{ccc}x&if&x>0\\ \alpha(e^{x}-1)&if&x\leq 0\end{array}\right. \tag{3}\]  
In Figure 1, the hidden layer in the middle represents the context when the input data is context. Focusing on this, parts of the newly proposed model in Section 3.2 will be learned.  
Figure 1: Context based Autoencoder.
# An Embedded Sensor Node Microcontroller with Crypto-Processors
## 3 Microcontroller Design
### Design Architecture  
TNODE5 includes the on-chip peripherals such as standard serial interfaces (UART and SPI), a 16-bit and a 32-bit timer, digital IO ports, communication and crypto accelerators as well as controllers for the integrated ADC and Flash memory. The chip was designed to fit into a 64-pin package, where all the peripheral pins, except ADC ports, are shared with the general-purpose IO pins. A smaller footprint of the chip provides better integration capabilities and helps reducing the overall size of an assembled sensor node. The architecture of TNODE5 is shown in Figure 2.
# Research on Variable Universe Fuzzy Multi-Parameter Self-Tuning PID Control of Bridge Crane
## 3 Variable Domain Fuzzy Multi-Parameter Self-Tuning PID Controller  
### Variable Domain Fuzzy Control  
After determining the structure of a conventional fuzzy controller, its fuzzy control rules, input/output variable fuzzy domains, and fuzzy subset partitions are fixed. However, in situations where the input error and the error change rate are both small around the steady state, the fuzzy subset partition of the input variable fuzzy domain may be too coarse compared to the small error. This can result in the controller repeatedly using only a few control rules, which can negatively impact control precision at the steady state.  
To address this issue and achieve the desired output without altering the fuzzy subset partition or fuzzy control rules, the input/output variable fuzzy domains must be adjusted in accordance with changes in the error. Specifically, when the error is large, the basic fuzzy domain is appropriately expanded, while it is compressed when the error is small. This approach is equivalent to adding fuzzy control rules and ensuring output accuracy.  
Assuming the initial fuzzy domains for the input error \(e\) and error change rate \(ec\) are \([-E,E]\) and \([-EC,EC]\), respectively, and the initial fuzzy domain for the output variable u is \([-U,U]\). By introducing domain scaling factors, the domains can be represented as:  
\[X(e)=[-\alpha(e)E,\alpha(e)E] \tag{9}\]  
\[X(ec)=[-\alpha(ec)E,\alpha(ec)E] \tag{10}\]\[\mathrm{Y}(y)=[-\beta(y)U,\beta(y)U] \tag{11}\]  
The variables \(\alpha(e)\), \(\alpha(ec)\), and \(\beta(y)\) represent the domain scaling factors for the input variables \(e\) and \(ec\), and the output variable \(u\), respectively.
## 3 Methodology
### Model Metrics Evaluation  
Additionally, the mean square error (MSE) and Pearson correlation coefficient (CC) were chosen as accuracy measures in this study to check how well the models (BLI inversion, empirical, and SVR models) worked. Mean Squared Error (MSE) is a widely used metric for assessing the accuracy of a regression model. It is computed as the mean of the squaresof the differences between the predicted values and the actual values. The mathematical formula is as follows:  
\[MSE=\frac{1}{n}\sum_{k=1}^{n}(y_{k}-\hat{y}_{k})^{2}, \tag{21}\]  
where \(n\) is the total number of samples in the dataset, \(y_{k}\) is the dependent variable's actual value for sample \(k\), and \(\hat{y}_{k}\) is the value that the model predicted for sample \(k\). The Pearson correlation coefficient, denoted as CC, is a statistical measure quantifying the strength and direction of the linear relationship between two variables. The mathematical formula for calculating the Pearson correlation coefficient is as follows:  
\[CC=\frac{\sum_{i=1}^{n}(x_{i}-\bar{x})(y_{i}-\bar{y})}{\sqrt{\sum_{i=1}^{n}(x_ {i}-\bar{x})^{2}\sum_{i=1}^{n}(y_{i}-\bar{y})^{2}}}, \tag{22}\]  
where n is the number of paired observations, \(x_{i}\) and \(y_{i}\) are the individual data points for variables X and Y, and \(\bar{x}\) and \(\bar{y}\) are the means of variables x and y, respectively.
## 2 Motor Topology and Demagnetization Analysis
### Basic Model Demagnetization Simulation Analysis  
NdFeB permanent magnets are prone to irreversible demagnetization when exposed to high temperatures and in the presence of a demagnetizing magnetic field, which significantly impacts the performance of the motor. The basic model has a rated RMS current of 2 A. The demagnetization analysis conducted in this paper is performed at 8 times the rated current and a temperature of 200 \({}^{\circ}\)C. The defining formula for demagnetization rate is shown in Equation (2) [30]:  
\[\delta= \left(1-\frac{B_{r}^{\prime}}{B_{r}}\right)\times 100\% \tag{2}\]  
where \(B_{r}^{\prime}\) is the remanence of the permanent magnet after high temperatures and high input currents and \(B_{r}\) is the remanence of permanent magnets in the initial state.  
Figure 3 shows the distribution of the permanent magnet demagnetization rate for the basic model at 8 times the rated current and a temperature of 200 \({}^{\circ}\)C. From Figure 3, the permanent magnet in the second layer of flux barrier ribs on the left side of the rotor single-pole is demagnetized most heavily, with the maximum demagnetization rate of 82.69%. The  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Unit** & **Basic Model** \\ \hline Pole/Slot & / & 8/48 \\ Stator outer diameter & mm & 120 \\ Stator inner diameter & mm & 80 \\ Rotor outer diameter & mm & 79 \\ Rotor inner diameter & mm & 30 \\ Air-gap length & mm & 0.5 \\ Motor axial length & mm & 140 \\ Rated Current (RMS) & A & 2 \\ Rated RPM & r/min & 1500 \\ Rated average torque & N-m & 10.55 \\ Permanent magnet & / & N42 SH-R \\ Steel sheet & / & 50 JN1300 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the basic model.  
Figure 2: Simplified vector diagram of the basic model002E.  
maximum demagnetization rate of the permanent magnet in the third layer of flux barrier ribs is 24.46%. Serious irreversible demagnetization can directly affect the performance of the motor, resulting in reduced torque performance. The anti-demagnetization performance of the basic model is poor. In this paper, the basic model will be optimized to improve its anti-demagnetization performance by changing the rotor structure.
## 4 Case Studies: Part I: Simulation Analysis
### Noise Robustness Performance Evaluation  
Noise environments can easily distort PQD signals, interfering with the algorithm's ability to accurately identify their true types and leading to a decline in classification performance. Therefore, to evaluate the anti-noise capability of the algorithm, based on the classification performance results of the model in different SNR  
As can be seen in Figure 8 and Table 8, the recognition rate of C0-Normal has not reached 100%. This is mainly because, in a 20 dB strong noise environment, the noise interference causes relatively severe distortion of the signal waveform, resulting in high-frequency fluctuations in its amplitude. In other words, the corresponding LT deformation characteristics of C0 are similar to those of C5-Oscillatory transient. Therefore, three groups of samples are identified as C5. Meanwhile, due to the presence of clearly distinguishable amplitude deviation components (C1/C2/C3) and C5 and C8, three groups of double disturbances (C11/C21/C22) can be identified with 100% accuracy. On this basis, three triple disturbances involving C4 (C27/C30/C33), which also have excellent feature representation capabilities in strong noise environments, can likewise achieve 100% classification accuracy. In addition, the samples of C25 and C34 are accurately classified due to the presence of clearly characterized C6-pulse and C7-Flicker, which have similar features to C5 in a strong noise environment. On the other hand, the proposed algorithm performs poorly in the classification of C28, C31, and C36, with classification accuracies below 70%. This is mainly because the waveform fluctuation characteristics of C7 under a strong noise  
Figure 8: Confusion matrix of 41 types of PQD classification results in a strong noise environment of 20 dB. The background color indicates the quantity of classification results; the darker the color, the greater the number of samples in the corresponding category.  
environment of 20 dB are highly similar to those of C5. When both appear on the same waveform, the characteristics of the former are easily masked by the latter, thereby causing confusion in determination. Therefore, 56 and 11 groups of samples in C28 are misclassified as C27 and C36, respectively, 59 and 16 groups of samples in C31 are determined as C30 and C38, respectively, and 78 groups of samples in C36 are confused as C27.  
Overall, despite certain accuracy limitations for specific disturbance types in a strong noise environment of 20 dB, the proposed algorithm framework still achieves an overall PQD classification accuracy of 93.86%, demonstrating excellent classification performance and noise robustness.
# Automated MRI Video Analysis for Pediatric Neuro-Oncology: An Experimental Approach
## 2 Materials and Methods
### AI System Evaluation Methodology
#### 2.3.4 Set of Questions 3: Detailed Questions  
1. _I am uploading a video file of a brain MRI with contrast showing a tumor. Are you able to analyze this video and identify the pathological change?_  
Rationale: This question is the most detailed and targeted. It informs the AI model of the specific context of the image (brain MRI with contrast showing a tumor) and requires pathological analysis. Such a question tests the AI models' ability to not only recognize general image features but also identify specific pathological changes.  
Gradating the questions from general to specific is crucial in assessing AI models' abilities to interpret medical images at different levels of detail (Figure 1). The initial sets of questions allow for a general assessment of the models' capacity to process and interpret images, while the subsequent sets of questions enable a more thorough analysis and evaluation of the model's ability to identify specific pathological features. This approach is systematic and allows for a comprehensive evaluation of the AI models' performance in a medical context.
## 3 Results
### Change of ZnO Concentration from 2.00:1.00 \(\rightarrow\) 1.94:1.00  
As a first step to modulate the band gap of Zn\({}_{2}\)TiO\({}_{4}\), we optimize the ZnO concentration in small steps to 1.98:1.00 (sample A05), 1.96:1.00 (sample A06), and 1.94:1.00 (sample A07). The pXRD results of the three samples are shown in Figure 2a,b. The reason for this choice is the already reported impact of such modification on the crystallinity of the samples and the improvement of the persistent luminescence properties of doped ZnGa\({}_{2}\)O\({}_{4}\) samples [1; 2]. Looking at the results in Figure 2a, the positions of the prominent diffraction peaks remain the same in all samples, suggesting that the prominent crystal structure is not distorted upon a small change in the ZnO concentration. However, a decrease in the reflection intensity of unreacted ZnO-related peaks is clearly noticed. A reflection at 31.75 \(2\theta\) values is shown in Figure 2b for better visualization of this inference. The effect of such modification on the UV-Vis absorption bands (or band gap) is discussed in later sections.  
Figure 1: (**a**) The pXRD pattern of four samples annealed at different temperatures; (**b**) zoom-in showing extra peaks of small intensity at 31.75 and 36.25 \(2\theta\) degrees which corresponds to ZnO.  
Figure 2: (**a**) pXRD pattern of three samples with ZnO:TiO\({}_{2}\) ratio of 1.98:1.00, 1.96:1.00 and 1.94:1.00 annealed at temperature 800 \({}^{\circ}\)C; (**b**) zoom-in showing extra peaks of small intensity of ZnO.
# An Approach to Multi-Objective Path Planning Optimization for Underwater Gliders
## 4 Results
### Grouped Scenario Analysis
#### 4.2.2 Favourable vs. against Ocean Currents  
Selecting the ocean currents direction as grouping factor, favourable currents scenarios 1, 2, 3, and 4, on one side, and scenarios 5, 6, 7, and 8, on the other, have been compared. The mult-compare graphs are shown in Figure 21. Configuration 16 is the top ranked, producing better statistically significant results for all other alternatives, except 13, 17, 18, 22, 25, and 26 in favourable group, and 13, 14, 17, and 18 for against group.
## 2 Methodology
### Saliency Detection in Frequency Domain
#### 2.2.3 Ultimate Saliency Map  
We fuse the global and local saliency maps in a simple way:  
\[P_{f}=\gamma P_{g}+(1-\gamma)P_{l}, \tag{9}\]  
where \(\gamma\) is the weight of the global saliency map, \(P_{f}\) is the ultimate saliency map, i.e., the result of saliency detection.  
Figure 2 intuitively shows the entire process. In Figure 2a, huge and abrupt reflections (at the top and middle of the image) make floating objects (at the bottom of the image) inconspicuous. Figure 2b shows that gamma correction handles the illumination problem well. In Figure 2c, most of the non-salient parts are obviously suppressed by Gaussian high-pass filtering. Figure 2d shows that phase spectrum reconstruction makes salient regions relatively concentrated and makes redundant parts dispersed. Figure 2e,f shows the binary images of global saliency map, local saliency map, and ultimate saliency map, respectively. Note that the method of binarization are all Otsu. In Figure 2e,f, global or local decomposition can remove most of the redundant parts, but it is not able to remove all redundancies. However, as shown in Figure 2g,h, through a fusing operation, the final result is much closer to the ground truth.  
Figure 2: Each stage of saliency detection. They are (**a**) the source image, (**b**) Gamma corrected image, (**c**) Gaussian high-pass filtered image, (**d**) phase reconstructed image, (**e**) global saliency map, (**f**) local saliency map, (**g**) ultimate saliency map, (**h**) result of our saliency detection.  
Since it is completely independent of training samples, our saliency detection method can be applied to detect any floating objects. As shown in Figure 3, even if the floating object in the scene is indistinguishable to the naked eye, or the scene has many targets, our method can successfully detect different unknown floating objects. In Figure 3a,b, the low contrast duck on the turbulent surface was detected. In Figure 3c,d, multiple floating litters of different sizes on the clam surface were detected. Note that the smallest object detected in Figure 3d is approximately 10x10 pixels in size, indicating that our saliency detection method has potential in small object detection.
## 3 Results and Discussion
In the areas of disturbance on the right side, an average Dv0.5 V 417.6 \(\upmu\)m was noted, resulting in very coarse droplets (404-502 \(\upmu\)m), while for the left side an average volume median diameter Dv0.5 value of 347.9 \(\upmu\)m was noted, which corresponds to the coarse droplet class (341-403 \(\upmu\)m). It can be concluded that according to the spray spectrum, the droplet class within the fine-medium range dedicated to this group of nozzles (RS11003) was obtained for the entire stream. However, in areas where special attention was paid, an increase in liquid volume was demonstrated as a result of testing the transverse distribution, which results from a clear increase in droplet diameter in the spectrum, despite the reduced occurrence. Studies have shown a significant change in droplet size, ranging from the medium level used to assess the entire stream, to coarse and very coarse droplets in disturbance areas. This affects the spray structure. Significant differences in the droplet size in individual spraying bands can affect the biological effectiveness of the plant protection product used. In such cases, the threat to the natural environment is very significant as a result of concentration (overlapping) of spraying bands of increased volume, especially in the assembly of nozzles on the spraying boom. The variability of the median volume in this range affects the liquid retention on plants, dripping, and penetration of droplets in the crop. Figure 10: Liquid volume distribution determined on the basis of D30 diameters obtained in 2D-PDA analysis of the size and number of drops for nozzle number 2. Figure 9: Distribution of volume diameters (Dv0.1, Dv0.5, Dv0.9) at measuring points in the liquid stream for nozzle number 2. Note: Dv0.1, volumetric diameter, below which smaller drops represent 10% of the total volume; Dv0.5, volumetric diameter, below which smaller drops represent 50% of the total volume; Dv0.9, volumetric diameter, below which smaller drops represent 90% of the total volume.
# Enhancing Design for Additive Manufacturing Workflow: Optimization, Design and Simulation Tools
## 4 Optimization Strategy
### Lattice Infill Optimization  
Lattice structures are very attractive for industrial lightweight applications [64]. Namely, the hip implant prosthesis is optimized with internal lattice infill to reduce the physical phenomenon of stress shielding and promoting excellent biocompatibility [65]. Indeed, the porous infill pattern gives the advantage of high specific strength, enhanced stiffness, superior capacity of energy absorption and offers the possibility to add internal functionality. Many mathematical models are available to generate porous patterns. Namely, it is possible to recognize periodically distributed porous patterns as the triply periodic minimal surfaces (TPMS) where uniform or functionally graded designs are created [66]; and random porous distribution through stochastic point cloud-based and fractal geometry that mimics the real porous distribution of nature scaffolds [67].  
This type of structure is included in the so-called multi-scale design, where the overall performance is evaluated using information from different abstraction levels. In fact, they can be considered as composite material with mechanical properties calculated via homogenization techniques [68]. Homogenization-based structural optimization is even one of the mathematical foundations for density interpolations schemes. This method converts the isotropic material TO analysis into a composite material consisting of infinite small holes, periodically distributed through the material. In this manner, TO is converted into a sizing optimization problem using micromechanical modelling where the design variable is described by several sub-variables to be optimized [69].  
To include lattice structures into the TO process, proper steps are required to combine both topology and size optimization [70]. The sequence starts by implementing a TO process to find the density field domain, then a density threshold is applied to preserve areas with high-density regions describing solid boundaries; the remaining domain serves as input to lattice wireframe generation. After the generation of infill lattice contours, a size optimization with adequate meshing and FEM simulation is applied. The result of the process is a graded infill lattice in low-density regions that has better performance with minimum support generation. A similar process is used inside commercial software nTop (nTopology, New York, NY, USA) [71].