esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of a Mechanism Used to Operate an Oscillating Separator
## 4 Theoretical Considerations  
The mechanism analyzed in this article is presented in Figure 5. It is a six-bar Stephenson mechanism. As illustrated in Figure 5, the coordinates of coupling A (base A) are not provided, as this point was selected to be located at the origin of the coordinate system (i.e., at the center). Consequently, the coordinates of the other components of the mechanism were determined by taking this point into account. In order to perform the requisite calculations, it was necessary to identify the following elements from a geometrical standpoint:  
* The coordinates of fixed points were provided: D (xo, yo) and G (x1, y1);
* The dimensional values of the mechanism elements were as follows: AB--a; BC--b; CD--c; BE--e; CE--d; EF--g; FG--f;  
Figure 4: Methodology of the work. The angle generated by the crank AB in relation to the horizontal (OX axis) was noted in the calculation with \(\alpha\);  
The ECB ternary element was regarded as a rigid element. The geometrical analysis of the six-bar mechanism permitted the determination of the calculation relationships for the following kinematic torques: C, E, and F. The computational relationships of the kinematic torque coordinates, which were previously imposed, were obtained corresponding to the projection on the two axes OX and OY. The results of the calculation relations, expressed in terms of the coordinates of the point in question, are displayed below:  
For coupling C (Equations (1) and (2)):  
\[x_{C} = a\cdot cos\propto+\frac{\left(b^{2}-c^{2}+d_{o}^{2}\right)\cdot \left(x_{0}-a\cdot cos\ \alpha\right)}{2\cdot d_{o}^{2}}-\frac{\sqrt{b+c+d_{o}}\cdot\sqrt{b+c-d_{o}} \cdot\sqrt{b-c+d_{o}}\cdot\sqrt{-b+c+d_{o}}\cdot\left(-y_{o}+a\cdot sin\ \alpha\right)}{2\cdot d_{o}^{2}} \tag{1}\] \[y_{C} = a\cdot sin\propto-\frac{\sqrt{b+c+d_{o}}\cdot\sqrt{b+c-d_{o}} \cdot\sqrt{b-c+d_{o}}\cdot\sqrt{-b+c+d_{o}}\cdot\left(-a\cdot cos\ \alpha+x_{o}\right)}{2\cdot d_{o}^{2}}+\frac{\left(b^{2}-c^{2}+d_{o}^{2} \right)\cdot\left(y_{0}-a\cdot sin\ \alpha\right)}{2\cdot d_{o}^{2}} \tag{2}\]  
For coupling E (Equations (3) and (4)):  
\[x_{E} = u_{2}+\frac{\left(b^{2}+d^{2}-e^{2}\right)\cdot\left(-u_{2}+a\cdot cos \ \alpha\right)}{2\cdot b^{2}}-\frac{\sqrt{b+d+e}\cdot\sqrt{b+d-e}\cdot\sqrt{b-d+ e}\cdot\sqrt{-b+d+e}\cdot\left(v_{2}-a\cdot sin\ \alpha\right)}{2\cdot b^{2}} \tag{3}\]  
\[y_{E} = v_{2}-\frac{\sqrt{b+d+e}\cdot\sqrt{b+d-e}\cdot\sqrt{b-d+e}\cdot\sqrt{-b +d+e}\cdot\left(a\cdot cos\ \alpha-u_{2}\right)}{2\cdot b^{2}}+\frac{\left(b^{2}+d^{2}-e^{2}\right)\cdot \left(-v_{2}+a\cdot sin\ \alpha\right)}{2\cdot b^{2}} \tag{4}\]  
With regard to coupling F, the calculation equations are as follows (Equations (5) and (6)):  
\[x_{F} = u_{3}+\frac{\left(-f^{2}+g^{2}+d_{5}^{2}\right)\cdot\left(x_{1}-u_{3} \right)}{2\cdot d_{5}^{2}}+\frac{\sqrt{f+g+d_{5}}\cdot\sqrt{f+g-d_{5}}\cdot \sqrt{f-g+d_{5}}\cdot\sqrt{-f+g+d_{5}}\cdot\left(-y_{1}+v_{3}\right)}{2\cdot d _{5}^{2}} \tag{5}\]  
Figure 5: The components of the mechanism under study.
## 3 Experimental Results
### Comparison with Prior Works  
As a closely related work, [PERSON] et al. [22] focus on identifying selected functional networks in 21 healthy volunteers by training a simple feed-forward neural network model. This approach was achieved using a Multilayer Perceptron (MLP), which usually follows the procedure of hand-crafted features extracted from data. Generally, Multilayer Perceptrons (MLP) are fully connected neural networks which generate outputs based on inputs. Literature sometimes uses MLP interchangeably with Deep Neural Network (DNN); however, there is a sharp contrast because MLP is a subset of DNN. In this case, there is a pre-selection of ICs of interest. Our ICs were generated using a bottom-up, data-driven approach using an independent component analysis. ICA has gained popularity as one of the two frequently selected analytical methods for rs-fMRI data, which requires no seed on any predefined region [54; 55]. In contrast, ICs generated in [PERSON] et al.'s [22] study used canonical seed regions of interest scattered across the brain. These two approaches may provide similar features for further analysis. However, hand-crafted feature extraction can limit the flexibility and potential of identifying certain functional brain areas, as demonstrated in our approach. In addition, the location of the seed regions could significantly impact the resulting pattern of a functional system such as the Language network. Furthermore, sensitivity to systematic noise such as head movement and physiological nuisance signals causes false identification of non-language areas (false positive) and false detection of putative language areas (false negative), which limits the clinical application of seed-based rs-fMRI in language mapping [30]. The comparison of our proposed CNN performed in the same conditions as [PERSON]'s work and the method of [22] is given in Table 4 and demonstrates the interest of our approach.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Proposed CNN** & **T. [PERSON] et al. [22]** \\ \hline Training: Healthy—Test: Healthy & \(0.86\pm 0.02\) & \(0.84\pm 0.01\) \\ \hline Training: Unhealthy—Test: Unhealthy & \(0.75\pm 0.01\) & \(0.72\pm 0.01\) \\ \hline Transfer Learning from Healthy to Unhealthy & \(0.74\pm 0.01\) & \(0.71\pm 0.01\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the proposed model with the alternative method.  
Figure 7: Validation accuracy curve of unhealthy data added to model training of healthy data.
# Comparison of GPM IMERG and TRMM 3B43 Products over Cyprus
## 2 Study Area  
Located in the north-eastern corner of the Mediterranean Sea, the island of Cyprus has a typical eastern-Mediterranean climate. The major characteristic of this type of climate can be concisely described by a bimodal seasonality with alternating relatively short wet winters and prolonged dry summers. As can be seen from the geomorphological map in Figure 1, the island is transversed by two mountain ranges: the high Troodos massif in the southwest with the highest peak, Olympus at 1951 m, and the elongated east-west oriented narrow Pentadaktylos range, rising to 900 m which borders the northern coast from east to west. Between the two mountain ranges, lies the central Mesaoria plain. Narrow, relatively flat strips of land surround the island along its coast.  
Figure 1: Geomorphology map of Cyprus highlighting the distribution of the dense gauge station network grouped according to elevation.  
Most of the winter dynamic systems which affect Cyprus originate from the southwest to west [31; 32]; hence, the highest average annual precipitation values are recorded on the southern side of the highest peaks of the Troodos mountain and the lowest over the rain-shadowed areas north of Troodos and at coastal stations on the east part of the island [33].
# Study on the Prediction Method of the Ultra-Low-Cycle Fatigue Damage of Steel
## 2 Theoretical Model for ULCF of Structural Steel
### Calibration of Material Parameters for CVGM  
#### 2.5.1 Cycle Loading Test for Notched Round Bar Specimen  
Cyclic loading tests of the notched round bar specimens are required to calibrate damage degradation parameter \(\lambda_{CVGM}\). The loading form is constant amplitude strain loading, and the strain ratio equals \(-1\). The loading form and fatigue life \(N_{f}\) are recorded in the test [33].
# Mixer U-Net: An Improved Automatic Road Extraction from UAV Imagery
## 2 Preliminaries
### Building Blocks
#### 2.1.1 U-Net for Biomedical Image Segmentation  
As mentioned before, the U-Net model was originally proposed for biomedical image segmentation. The model consists of contracting path (encoder) that compresses the input data into low-dimensional representation and an expansive path (decoder) that decodes extracted information back to the original size. Both contracting and expansive path consist of four stages and in the middle, and there is a bridge comprising two \(3\times 3\)convolution layers that connect them. At each stage of contracting path, there are two cascaded \(3\times 3\) convolution layers to extract spatial features followed by a max-pooling operation that halves the resultant feature map. ReLU activation function is performed after all \(3\times 3\) convolution layers. After each max-pooling operation, the filter size is doubled. On the other hand, at each stage of expansive path, before upsampling the feature maps with transposed convolution operation, the filter size is halved. After the upsampling process, the output is concatenated with the extracted feature map of the corresponding stage of the contracting path that enables conveying low-level spatial information to high-level layers. Aggregated information is convolved by two consecutive \(3\times 3\) convolutions. At the last stage, following the two \(3\times 3\) convolution operations, \(1\times 1\) convolution is performed to generate the final output. It is important to point out that, in our implementation, we incorporate Batch Normalization layer [27] as a method to accelerate the training process, which was not used in the original implementation of U-Net as it did not exist at that time.
## 4 Foundations of Recommendation Systems and LLM Integration
### Large Language Models: Capabilities and Evolution
#### 4.2.4 XLNet  
XLNet stands out for its advantages in text classification and recommendation tasks, owing to its hybrid architecture that integrates autoregressive and autoencoding capabilities. XLNet understands complex language patterns and user behaviour by effectively capturing bidirectional context, making it ideal for tasks requiring deep comprehension and precise predictions. In text classification, XLNet's permutation-based training enables the model to capture bidirectional context more effectively than BERT or RoBERTa. This advantage is crucial for tasks involving intricate semantic relationships, as XLNet demonstrates superior performance on datasets with complex language structures [30; 31]. Its ability to handle long-range dependencies enhances its performance in tasks like sentiment analysis and information retrieval, allowing it to outperform traditional models when interpreting extended text sequences [30; 31]. Moreover, XLNet's robustness across various datasets, such as AG News and TREC-QA, highlights its adaptability and high performance in diverse classification challenges [31]. In recommendation tasks, XLNet's bidirectional architecture enables it to model both long-term and short-term user interests effectively, making it highly suitable for dynamic environments like e-commerce, where user preferences frequently shift [32; 33; 34]. Furthermore, XLNet efficiently incorporates side information, such as demographic data or user behaviour patterns, improving the relevance and personalization of recommendations, a limitation of many traditional models [34]. Despite its many strengths, XLNet does come with increased computational complexity and resource demands. In scenarios where computational efficiency is paramount, models like BERT and RoBERTa may still be preferred due to their ability to balance performance and resource consumption [35].
# OFDM Emitter Identification Method Based on Data Augmentation and Contrastive Learning
## 3 Proposed Method
### Data Augmentation  
Subcarrier rerank. Aiming at the characteristic that the OFDM signal contains 108 subcarriers, we propose a method by adjusting the order of subcarriers to generate extra samples. The specific method is shown in Figure 5: Randomly generate N random sequences ranging from 0 to 108, adjust the order of subcarriers of each original sample according to this sequence, and ensure that each signal generates new samples of the same subcarrier sequence. This method expands the number of samples in the training set to N+1 times than the original, so as to achieve the purpose of data augmentation.  
Time domain flip. In terms of the periodicity of the signal wave, we propose a data augmentation method that numerically flips the time-domain signal to generate new samples. The 1200 symbols of each subcarrier in the original signal are horizontally flipped, and the flipped subcarriers are recombined into an OFDM signal containing 108 subcarriers to become a new training sample.  
Phase domain flip. In the phase domain, Hilbert Transform is a common method in signal processing, which reverses the phase of the original signal and shows different perspectives of the signal. We adopt the Hilbert transform method, manually carrying out a convolution to construct the time-frequency analytic signal of the target. The Hilbert  
Figure 4: Data preparing. We tried plenty of data types, including spectrum, one-channel (amplitude), and two-channels (amplitude and phase).  
Figure 5: Data augmentation: Subcarrier rerank, Randomly adjust the order of subcarriers of OFDM signals.  
transform is essentially a 90-degree phase shifter. Supposing the existing signal \(x(t)\), then the Hilbert transformation of the signal is defined as \(H[x(t)]\):  
\[H[x(t)]=\frac{1}{\pi}\int_{-\infty}^{+\infty}\frac{x(\tau)}{t-\tau}d\tau. \tag{1}\]
# Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior
###### Abstract  
The smart meter is an important part of the smart grid, and in order to take full advantage of smart meter data, this paper mines the electricity behaviors of smart meter users to improve the accuracy of load forecasting. First, the typical day loads of users are calculated separately according to different date types (ordinary workdays, day before holidays, holidays). Second, the similarity between user electricity behaviors is mined and the user electricity loads are clustered to classify the users with similar behaviors into the same cluster. Finally, the load forecasting model based on the Online Sequential Extreme Learning Machine (OS-ELM) is applied to different clusters to conduct load forecasting and the load forecast is summed to obtain the system load. In order to prove the validity of the proposed method, we performed simulation experiments on the MATLAB platform using smart meter data from the Ireland electric power cooperation. The experimental results show that the proposed method is able to mine the user electricity behaviors deeply, improve the accuracy of load forecasting by the reasonable clustering of users, and reveal the relationship between forecasting accuracy and cluster numbers.  
smart meter; clustering algorithm; load forecasting; OS-ELM; electricity behavior analysis +
Footnote †: journal:
# The Challenges of Advanced CMOS Process from 2D to 3D
## 2 Lithography of Nano-Scaled Transistors
### Challenges in ArF lithography with Multi-Patterning [12]
#### 2.1.2 Mask with Reticle Enhancement Techniques (RETs)  
In principle, down scaling the transistor dimensions results in more difficult mask making. To deal with the diffraction issues, various RETs must be used on the mask. One RET with optical proximity correction (OPC) is used to modify the mask patterns and to improve the printability on the wafer. OPC makes the use of assist features, which they are getting smaller and more complex at each technology node. More aggressive OPCs, e.g., inverse lithography techniques (ILT) or shapes approaching ILT are needed to get the required process window. This makes the mask shapes more complex and it requires finer geometries and spacing for the mask.
# W-IFL: An Improved Maximum Power Point Control Model to Promote Renewable-Powered Vehicles
## 3 The W-IFL Methodology
### Wavelet Predictor  
As designed in Figure 1B, the wavelet predictor is a 3-layer network. This is due to the fact that the output characteristic curve of a PV cell is nonlinear in time, which cannot be approximated by less than 3 layers, and more layers will increase the computation complexity exponentially [41].  
Combing the discrete-time data illustrated in Table 1, hence, to obtain three kinds of information in terms of (a) smooth & continuous wavelet amplitude, (b) amplitude, and (c) phase of the temporal PV system, the non-orthogonal and complex-valued wavelet function, as described in Equation (1), is chosen as the transfer function \(\varphi(t)\), namely the Morlet function [42].  
\[\varphi(x)=Ce^{\frac{x^{2}}{2}}\cos(5x);(C\;is\;a\;constant) \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Temperature (\({}^{\circ}\)C)** & **Time** & **Irradiance (\({\mathbf{l}}\)x)** & **PV Cell Output Voltage (\({\mathbf{V}}\))** \\ \hline
34.0 & 9:24 & 767,000 & 38.53 \\
34.0 & 9:45 & 785,000 & 37.33 \\
35.1 & 11:20 & 810,000 & 30.05 \\
36.0 & 13:39 & 214,000 & 28.19 \\
34.8 & 14:40 & 1,000,000 & 28.15 \\
35.0 & 14:43 & 974,000 & 28.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data example of PV Cell Output.  
Figure 1: (**A**) The schema of the proposed MPP control; (**B**) The framework of the W-IFL MPPT model (Icon Source: [[https://www.flaticon.com/](https://www.flaticon.com/)]([https://www.flaticon.com/](https://www.flaticon.com/)) (accessed on 25 October 2022)).  
Temperature, irradiance, and time are represented in the input layer via \(x_{1}\) to \(x_{3}\), respectively. The number of neurons in the hidden layer is \(n\) (\(n=1,\ 2,\ 3,\ldots\)), and only one neuron exists in the output layer, namely the predicted voltage at MPP.  
Notably, once set the following parameters: \(w\) is the weight, \(i\) & \(j\) represent two neurons, \(m\) expresses the layer number, \(b\) indicates the output of the last neuron, \(a\) means neuron's input, and \(j\) & \(k\) state the \(k^{th}\) output of neuron, \(j\), and the interlayer relations can be mathematically summarized as Equation (2).  
\[\begin{array}{l}a_{jk}^{m}=\sum\limits_{i}w_{jk}^{m}b_{ik}^{m-1}\\ b_{jk}^{m}=\varphi\left(a_{jk}^{m}\right)\end{array} \tag{2}\]  
In this 3-layer network, \(b_{jk}^{1}=x_{k-j+1}\) and \(b_{jk}^{3}=\hat{x}_{k+1}\), the predicted output, \(\hat{x}_{k}\), can be obtained through the Morlet function, as described in Equation (3).  
\[\begin{array}{l}a_{jk}^{2}=\sum\limits_{i=1}w_{ij}^{2}x_{k-j+1}\\ b_{jk}^{2}=\varphi_{2}\left(\frac{a_{jk}^{2}-b_{j}}{a_{j}}\right)\\ \hat{x}_{k+1}=\sum\limits_{j=1}b_{jk}^{2}w_{j}^{3}=\sum\limits_{j=1}\varphi_{ 2}\left(\frac{a_{jk}^{2}-b_{j}}{a_{j}}\right)w_{j}^{3}\end{array} \tag{3}\]  
Equation (4) uses the mean square error, \(\theta\), between the predicted and actual values as the objective function.  
\[\theta=\frac{1}{2}(\hat{x}_{k}-x_{k})^{2} \tag{4}\]  
Furthermore, the wavelet predictor adopts conjugate gradient descent to minimize \(\theta\) to achieve optimization.
# Multi-Scale Rolling Bearing Fault Diagnosis Method Based on Transfer Learning
## 2 Preliminaries
### Transfer Learning  
Transfer learning represents an advanced approach within the sphere of artificial intelligence, whose core principle is utilizing knowledge acquired from a specific task (i.e., the source task) to enhance or improve learning and performance in another related but different task (i.e., the target task) [28]. In the architecture of transfer learning, the source task refers to the task on which the model is initially trained and learns. This task usually has a large amount of labeled data, allowing the model to learn rich feature representations and deep data patterns through deep learning. The knowledge and patterns learned in this process build a knowledge base for the model, which can be applied and reused in subsequent different but related tasks. On the other hand, the target task refers to the new task that the model needs to adapt to or handle subsequently. Compared to the source task, the target task often faces challenges such as a smaller amount of data or difficulty in obtaining labeled data [29; 30; 31]. In such scenarios, training a model from scratch may be inefficient or even impractical. Hence, transfer learning provides a solution by transferring and applying existing knowledge to overcome the limitations of data in the target task.  
Suppose that there is a source task \(T_{S}\) and a target task \(T_{T}\), with a dataset \(D_{S}=(x_{s1},y_{s1},\)\(x_{s2},y_{s2}),\ldots,(x_{sn},y_{sn})\), where \(x_{s}\) represents feature vectors and \(y_{s}\) are the corresponding labels. For the target task, there is a dataset \(D_{T}=(x_{t1},y_{t1}),(x_{t2},y_{t2}),\ldots,(x_{tm},y_{tm})\). Typically, the size of \(D_{T}\) is smaller than \(D_{S}\). The goal of transfer learning is to use the knowledge from \(D_{S}\) to improve the recognition capability on \(D_{T}\).  
Our study applies the parameters learned in the source domain task to the target domain task, thereby enhancing the model performance in new tasks. This strategy is particularly crucial in deep learning applications, as it significantly reduces the dependence on large amounts of labeled data, while playing an important role in saving computational resources and time. Specifically, if \(\mu_{S}\) represents the parameter set trained in the source domain task, and \(\mu_{T}\) represents the parameters trained in the target domain task, the mathematical relationship between them can be expressed as  
\[\mu_{T}=F(\mu_{S}), \tag{1}\]  
Here, \(F\) is a function that defines how to transfer from \(\mu_{S}\) to \(\mu_{T}\). In the simplest case, this transformation can be achieved through direct parameter copying, i.e., by applying the parameters learned from source domain task directly to the target task. However, depending on the specific tasks and data differences, \(F\) may also involve more complex transfer logic, such as parameter fine-tuning or retraining strategies, to better adapt to the specific needs of the target task.