esa-sceva/satcom-chunk-collection huggingface dataset:
# Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform
## 1 Introduction
These shortcomings limit the RNN capability to fully learn the temporal dynamic behavior of the time series. Long short-term memory (LSTM) was proposed in [36] to overcome the gradient disappearance and the gradient explosion. As one of the improved variants of recurrent neural network, LSTM can learn the information contained in time series data more effectively. LSTM has been widely used in handwriting recognition [37], music generation [38], object detection [39], sentiment analysis [40], and speech recognition [41]. In [42], the LSTM was used for dynamic modeling of the time series in which the short-term prediction of wind power was carried out. In summary, the LSTM networks can effectively capture the dynamic behavior of time series data compared with the BP neural network and the support vector machine. However, if the LSTM network is directly used to learn and predict the original data, the prediction accuracy is not high. If the non-stationary wind power time series can be decomposed into several more predictable components with less non-stationarity, the prediction accuracy can be significantly enhanced. Among the decomposition approaches, wavelet decomposition [43] and empirical mode decomposition [44] have been effectively applied to the non-stationary time series prediction. In recent years, wavelet transform has been widely used in the fields of signal and image analysis, noise reduction, and compression [45; 46]. In [12], a spatiotemporal WPP model consisting of wavelet transform, two-input symmetric correlation, and the least squares support vector machine is proposed, which does not use LSTM. This paper presents a discrete wavelet transform and long short-term memory network (DWT_LSTM) WPP method based on LSTM and discrete wavelet transform (DWT). Its innovative contribution is to develop an accurate and efficient deterministic WPP strategy using a combination of a data filtering approach based on discrete wavelet transform (DWT) and the LSTM network. The strategy of the proposed DWT_LSTM consists of two stages. In the first stage, it decomposes the original wind power data into an approximate signal and some detailed signals through DWT. In the second stage, every sub-signal is assigned a separate LSTM for training. After that, the final prediction result is obtained by summing the corresponding predicted values of each sub-signal linearly. Based on the actual wind power data provided by three wind farms, the effectiveness and accuracy of the method are verified. The experimental results show that the prediction method proposed in this paper has higher prediction accuracy than the benchmark prediction methods. The rest of this paper is organized as follows. Section 2 presents the problem description; Section 3 proposes the DWT_LSTM forecasting method; Section 4 presents the experimental design. Numerical validation is presented in Section 5. Section 6 concludes the paper.
# Atlas-Based Evaluation of Hemodynamic in Ascending Thoracic Aortic Aneurysms
###### Abstract  
Atlas-based analyses of patients with cardiovascular diseases have recently been explored to understand the mechanistic link between shape and pathophysiology. The construction of probabilistic atlases is based on statistical shape modeling (SSM) to assess key anatomic features for a given patient population. Such an approach is relevant to study the complex nature of the ascending thoracic aortic aneurysm (ATAA) as characterized by different patterns of aortic shapes and valve phenotypes. This study was carried out to develop an SSM of the dilated aorta with both bicuspid aortic valve (BAV) and tricuspid aortic valve (TAV), and then assess the computational hemodynamic of virtual models obtained by the deformation of the mean template for specific shape boundaries (i.e., \(\pm 1.5\) standard deviation, \(\sigma\)). Simulations demonstrated remarkable changes in the velocity streamlines, blood pressure, and fluid shear stress with the principal shape modes such as the aortic size (Mode 1), vessel tortuosity (Mode 2), and aortic valve morphologies (Mode 3). The atlas-based disease assessment can represent a powerful tool to reveal important insights on ATAA-derived hemodynamic, especially for aneurysms which are considered to have borderline anatomies, and thus challenging decision-making. The utilization of SSMs for creating probabilistic patient cohorts can facilitate the understanding of the heterogenous nature of the dilated ascending aorta.  
ascribing aortic aneurysm; statistical shape analysis; computational fluid dynamic aca 202212394. [[https://doi.org/10.3390/app12010394](https://doi.org/10.3390/app12010394)]([https://doi.org/10.3390/app12010394](https://doi.org/10.3390/app12010394))
## 3 400G Pluggable Module Form Factors and Power Requirement
CFP: C form-factor, OSFP: octal small form factor, QSFP-DD: quad small form factor double density. the power consumption of DSP down to less than 2 W, which is larger than a 20 times reduction. This tremendous improvement can potentially fit the 400G-ZR QSFP-DD DCO under a 15 W power envelop. As shown in Table 2, DSP, TROSA, and the other components are allocated only 7 W, 6-7 W, and 1-2 W power budget, respectively. In such tight power and size constrains, the industry has adopted 10-12 dB link budget in the power limited unamplified case for up to 40 km reach.
# Instance Segmentation and Number Counting of Grape Berry Images Based on Deep Learning
## 1 Introduction
### Related Work  
Generally, traditional image segmentation methods have used typical image processing and machine learning algorithms [2; 3], which for grapes includes the identification of grape clusters based on color features [4] and grape berries based on geometric features [5], texture features [6], and integrated features [7]. Based on the color and texture features, combined with the texture and color characteristics of LBP for feature extraction, the K-nearest neighbor (KNN) algorithm was used to achieve the segmentation of red and white grape varieties [8]. Based on texture morphology, a high-resolution vineyard image was obtained by controlling artificial lighting at night. The number of grapes was calculated by segmenting the grape clusters from the image background by applying a threshold intensity level to the transformed image hue color layer [9]. The segmentation of red globe grapes and white grapes was achieved by detecting and locating grape clusters in color images using color mapping and morphological dilation techniques in natural environments [10]. The abovementioned literature has all achieved the segmentation of grapes; however, they all have certain limitations, and none of the individual grapes were segmented. The limitations were mainly that the color features were not suitable for distinguishing green grapes, the morphological features were difficult to identify when the fruit was occluded, and the texture features could be difficult to distinguish under certain lighting conditions.  
With the rapid development of deep learning, image recognition and detection are widely used in fruit images such as such as grapes [11], blueberries [12], kiwis [13], strawberries [14], waxberry [15], and apples [16]. The image segmentation model Mask R-CNN is applied to segment the grape clusters, grape leaves [17], and grape cluster [18]. In [14], a strawberry fruit detection and segmentation method based on the Mask R-CNN algorithm was proposed, and by analyzing the shape and edge features of the mask image generated by Mask R-CNN, they obtained the location of strawberry-picking points. In [15], based on a Mask R-CNN net-work, the segmentation of overlapping bayberry fruits was realized via data augmentation processing, which improved the segmentation accuracy of occluded and overlapping fruits. In [16], an identification method of an apple-picking robot system based on a Mask R-CNN network for the identification and segmentation of overlapping apples in an orchard environment was proposed. In [19], a semantic segmentation-based implementation of grape berry segmentation and counting was proposed. We focus on each individual grape berry; therefore, this paper develops the study with instance segmentation. The above research proves the feasibility of using deep learning to achieve grape berry grade segmentation.
## 2 Materials and Methods
### Analysis
#### 2.4.4 Temperature Measurements
Heterocyclic Aromatic Amine (HCA) Analysis Chemicals  
The following HCA standards were purchased from Toronto Research Chemical (Toronto, ON, Canada): IQ (2-amino-3-methylimidazo[4,5-f]quinoline), MelQx (2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline), 4,8-DiMelQx (2-amino-3,4,8-trimethyl-dazo[4,5-f]-quinoxaline), PhIP (2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine), and harman (1-methyl-9H-pyrido[3,4-b]indole). Norharman (9H-pyrido[3,4-b]indole) was purchased from Sigma (Switzerland). Methanol, acetonitrile, acetic acid, and ammonium hydroxide were purchased from Merck (Germany), high-performance liquid chromatography (HPLC) grade. Purified water was obtained from a water purification system, Zeneer Power I (Water Purification System, Human Corporation, Seoul, Republic of Korea, 0.2 \(\upmu\)m Capsule Filter HMC-DPL-S). Extraction of Heterocyclic Aromatic Amines  
The extraction and purification of HCAs were determined according to [34]. A total of 4 g of cooked coated chicken liver sample was used. The extraction and analyses were determined in duplicate.
## 3 Optimal Design of the FCPM Model
### Design Variables, Objective Functions and Constraints (Step I)  
First, to improve the performance of the basic FCPM model, design variables that can change the asymmetric waveform of the back-EMF into a symmetric structure are selected in the optimal design. There are various variables, but ultimately, the angle and polar angle of the PM were set as variables. In addition, to take advantage of the CP-type structure, the volume of one CP-type PM was fixed. As the thickness of the PM decreases, it becomes difficult to manufacture the permanent magnet. So, the thickness of the PM is fixed at 3 mm. Figure 5 shows the design parameters for optimization. As shown in Figure 5, the design variable x1 refers to the magnet angle (), x2 refers to the polar angle (), and Table 3 shows the range of design parameters.  
Figure 4: Optimal design process.  
Figure 5: Design variables of basic FCPM model.  
Table 4 lists the dimensional constraints for determining the magnet angle (x1). As shown in Table 4, R1 represents the inner radius of each magnet, and R2 represents the outer radius. Although the inner and outer radius were changed, the weight of each magnet was the same, and the thickness was limited to 3 mm.  
For optimization, the objective function is determined as the maximum of the back-EMF, as well as the minimum THD of the back-EMF and cogging torque. In addition, the magnitude (RMS value), THD of the back-EMF, and cogging torque were set as performance constraints. The objective function and constraints are as follows:  
1. Objective function: Maximize: - y1 (magnitude of back-EMF) Minimize: - y2 (total harmonic distortion (THD)), y3 (peak to peak value of cogging torque)
2. Constraints: - back-EMF (Vrms) > 29 Vrms, THD of back-EMF < 10 %, cogging torque < 0.13 Nm
## 4 Results and Discussion
### Hydraulic Forces Acting on the Impeller  
The operating stability of the primary impeller is of significance for the whole pump unit. Under different operating conditions, the radial and axial hydraulic forces acting on the primary impeller are shown in Figure 19. The primary impeller has a compound structure and constitutes a complex mechanical system. The lower and upper parts of the primary impeller interact with the mediums with different physical properties, and the geometries of the two parts are different. These complicate the hydraulic forces acting on the primary impeller. Furthermore, each blade channel in the upper part of the primary impeller receives particles discharged from the corresponding blade channel of the secondary impeller, so the input condition for the primary impeller is difficult to define.  
As shown in Figure 19, under different operating conditions, the \(y\) component of the radial force points unanimously toward the negative \(y\) direction. In the volute chamber, particles travel in the streamwise direction with the impeller rotation. Particles tend to accumulate at the lower part of the volute chamber, as demonstrated by the distribution of SVE. At \(q_{z}=3.12\) m\({}^{3}\)/min, the area occupied by water is reduced. Hence, the increase in the water velocity causes a decrease in the static pressure. At \(n=850\) and \(950\) rpm, the pressure at the right side of the impeller is higher than that at the left, especially at \(n=950\) rpm. Nevertheless, when the rotational speed increases to \(1050\) rpm, the tendency is overturned, as evidenced in Figure 19.  
Regarding the axial hydraulic force acting on the primary impeller, it is related to several parameters such as the impeller diameter, the pump head, the density of the mixture,  
Figure 19: Hydraulic forces acting on the primary impeller under different operating conditions.  
and the rotational speed of the impeller. As shown in Figure 19b, the axial force acting on the primary impeller increases with the feed rate of sand, and the tendency is insensitive to the rotational speed. Furthermore, when \(q_{s}\) remains constant, with increasing rotational speed, the axial force increases continuously.
## 2 The Mathematical Model  
We considered the following linear time-fractional advection-diffusion equation  
\[\frac{\partial^{\alpha}}{\partial t^{\alpha}}u(x,t)+K_{1}\frac{ \partial}{\partial x}u(x,t)-K_{2}\frac{\partial^{2}}{\partial x^{2}}u(x,t)=f( x,t),\quad a<x<b,\quad 0<t\leq T, \tag{1}\]  
with the initial and boundary conditions given by  
\[u(x,0)=\phi(x),\qquad a\leq x\leq b,\] \[u(a,t)=\varphi(t),\qquad u(b,t)=\psi(t),\qquad\qquad 0<t\leq T,\]  
where \(u\) is the field variable that can represent, for example, the solute concentration, and \(K_{1}\) and \(K_{2}\) are the constant fluid velocity and the dispersion coefficient, respectively. The time fractional derivative, \(\frac{\partial^{\alpha}}{\partial t^{\alpha}}u(x,t)\) is the \(\alpha\) order Caputo fractional derivative defined by  
\[\frac{\partial^{\alpha}}{\partial t^{\alpha}}u(x,t)=\frac{1}{\Gamma(1-\alpha) }\int_{0}^{t}\frac{\partial}{\partial s}u(x,s)(t-s)^{-\alpha}ds\qquad 0< \alpha<1. \tag{2}\]  
The function \(f(x,t)\) can be used to represent sources and sinks. \(\phi(x)\), \(\varphi(t)\) and \(\psi(t)\) are known smooth functions. We took \(K_{1}\
eq 0\) and \(K_{2}>0\) and we assumed problem (1) had a unique and sufficiently smooth solution under the above initial and boundary conditions.  
The fractional Equation (1) represents a suitable mathematical model for describing anomalous physical processes that exhibit fractional order behavior that varies with time or space and that cannot be modeled accurately by normal integer order equations. In fact, it has been treated by a number of authors as a useful approach for the description of transport dynamics in complex systems which are governed by anomalous diffusion and non-exponential relaxation patterns [33]. The TFADE is also used in groundwater hydrology research to model the transport of passive tracers carried by fluid flow in porous mediums [34] and in neurology [35]. It is notable that, when \(\alpha=1\), the model (1) reduces to the classical advection-diffusion-reaction equation  
\[\frac{\partial}{\partial t}u(x,t)+K_{1}\frac{\partial}{\partial x}u(x,t)-K_{2} \frac{\partial^{2}}{\partial x^{2}}u(x,t)=f(x,t),\qquad a\leq x\leq b,\qquad 0<t \leq T,\]  
used in order to describe several phenomena of relevant interest in many fields of applied sciences. It is a mathematical model that describes how the concentration of the substance distributed in a medium changes under the influence of three processes: advection, diffusion and reaction. In [36; 37; 38], a fractional step approach with variable time step was used in order to solve numerically mathematical models that describe evolution problems on computational domains of one or three space dimensions.
# Repulsive Force for Micro- and Nano-Non-Contact Manipulation
## 3 Results
### Influence of the Sphere Size on the Repulsive Force Measured  
The force distance measurements obtained for APTES-modified surface, with borosilicate spheres of different diameters (between 1 and 40 \(\upmu\)m) are presented in Figure 1A.  
In Figure 1A, a cantilever deformation was observed on a long distance (typically several micrometers) when the sphere was approaching the surface. This distance increased with the size of the sphere from a few nanometers for a 10 nm sphere to 12 \(\upmu\)m for a 40 \(\upmu\)m sphere. The variation of the repulsive force was similar to the one observed during previous experiments [28]. The size of the sphere glued on the tipless extremity influenced the repulsive force. Indeed, for a lower sphere diameter (10 nm), no significant repulsion was measured, but when the sphere diameter was increased, the repulsive force appeared and it increased with the sphere diameter (Figure 1A). It reached around 800 nN and 1.1 \(\upmu\)N for 10 \(\upmu\)m and 40 \(\upmu\)m spheres, respectively. The repulsion can be explained by the electrostatic repulsion of the positive charges, at pH 2, of the amine grafted on the tip and on the surface.  
Figure 1: AFM force–distance curves at pH 2 between two APTES surfaces (one flat surface and one spherical, glued on the tipless cantilever): (**A**) influence of the functionalized borosilicate sphere: 10 nm (red), 10 \(\upmu\)m (blue), and 40 \(\upmu\)m (green); (**B**) influence of the ionic strength for a 10 \(\upmu\)m borosilicate sphere diameter glued on the tipless: no salt added (blue), 10 mM (red), 0.1 M (green), and 1 M (pink). All the surfaces were functionalized by APTES.
# Design and Implementation of Health and Risk Level Assessment for Socially Disadvantaged Patients
## 3 Platform Design and Implementation
### Measurement Tools  
Using the conventionally popular screening tools, we were able to judge the users as moderate, weak, severe, and dangerous, if they fell within the score ranges listed below. Learning data were collected to build a depression detection model.  
1. PHQ-9: Patient Health Questionnaire-9 as a depression screening tool  
This is a depression screening tool, standardized and developed by [PERSON] et al. (1999). The depression screening tool consists of a total of nine items, and each item is composed of a four-point scale ranging from 0 to 3 points. A total score of 4 or less is none-minimal, 5 to 9 is mild, 10 to 14 or less is moderate, 15 to 19 is moderately severe, and 20 or more is severe. We also used the \"Patient Health Questionnaire-9\" as the depression screening tool consisting of a total of nine items, and each item is composed of a four-point scale ranging from 0 to 3 points. A total score of 4 or less is none-minimal, 5 to 9 is weak, 10 to 14 is moderate, 15 to 19 is severe, and 20 or more is dangerous.
2. GAD-7: Generalized Anxiety Disorder 7 as an Anxiety Scaling tool  
GAD-7 is a brief measure for assessing generalized anxiety disorder. An anxiety scale diagnosis was used as an adaptation of the Generalized Anxiety Disorder Assessment (GAD-7) tool developed by [PERSON] (2006). It is an easy-to-use, self-administered patient questionnaire that uses seven items to measure or assess the severity of generalized anxiety disorder (GAD). Each item asks the individual to rate the severity of his or her symptoms over the past two weeks. Each item is composed of a four-point scale ranging from 0 to 3 points. A total score of 4 or less is normal, 5 to 9 is mild, 10 to 14 is moderate, and 15 or more is dangerous. The GAD-7 score is calculated by assigning a score of 0, 1, 2, or 3 to the response categories of \"not at all\", \"several days\", \"more than half the days\", and \"nearly every day\", respectively, and adding together the scores for the seven questions. Scores of 5, 10, and 15 are taken as the cut-off points for mild, moderate, and severe anxiety, respectively. When used as a screening tool, further evaluation is recommended when the score is 10 or greater.
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 6 Security Analysis  
We utilized the BAN logic to evaluate the user authentication of our protocol and then we used the ROR model to prove the session key security. In addition, we performed AVISPA simulation to evaluate the security of our protocol to replay and MITM attacks.
# Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)
## 3 Generative AI for Threat Detection
### Generative Adversarial Networks (GANs)  
Generative Adversarial Networks are a type of DL-based generative model introduced by [PERSON] in his 2014 paper [60]. The core idea behind GANs is to train two competing neural networks--a generator and a discriminator--in a game-theoretic framework. The generator creates fake data, while the discriminator's task is to distinguish between real and fake data. Over time, the generator improves its ability to generate realistic samples, and the discriminator becomes better at identifying fakes, leading to a dynamic optimization process that ultimately results in high-quality generated data.  
The architecture of GANs consists of two primary components: the generator and the discriminator. The generator takes a random noise vector as input and transforms it into synthetic data, while the discriminator evaluates whether the input data is real or fake. As shown in Figure 5, the generator (G) learns to create data similar to the real data (X_data), and the discriminator (D) classifies inputs as real or fake. Both networks are trained simultaneously, with the generator aiming to minimize the discriminator's ability to distinguish between real and generated data, while the discriminator seeks to improve its accuracy in identifying fakes. This adversarial process continues until the generator produces realistic data that the discriminator can no longer reliably distinguish from real data [61].  
To provide a deeper understanding of the internal mechanics of GANs, this training can be conceptualized as a minimax game between the two networks, each with opposing goals. Initially, the generator produces poor-quality synthetic data from random noise. The discriminator evaluates both real and generated samples, generating feedback that guides the generator's updates. With each iteration, the generator adjusts its parameters to produce more realistic data, while the discriminator concurrently becomes better at detecting fakes. Over time, this competitive interaction leads to a dynamic equilibrium in which the generator produces data distributions that closely mimic real ones, and the discriminator's ability to distinguish real from fake becomes effectively neutralized. This adversarial learning paradigm underlies the GAN's strength in capturing and modeling complex data distributions with high accuracy.  
Figure 4: Types of Generative AI models.  
In the field of security, GANs can be applied to detect attacks by generating synthetic attack data that mimics real-world attack scenarios. By training on both real and fake attack data, the discriminator can improve its ability to identify malicious behavior in a system. GANs could be particularly useful in generating attack signatures for intrusion detection systems, enabling them to recognize and respond to new and previously unseen attacks. This capability can enhance the detection of sophisticated threats, such as zero-day attacks, by providing a broader range of attack samples for training the security models.