esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Discover Phase  
The goal of the discovery phase was to establish potential bottlenecks in the patient-user journey. This required interviewing physiotherapists who specialized in MSK rehabilitation and users who had undergone total knee reconstruction surgery followed by long-term rehabilitation. In order to design a solution that effectively addressed users' primary needs, it was first essential to understand the challenges users faced from their point of view and empathize with their experiences. Four user interviews were conducted to gain insights into these challenges and pain points. Through analysis of interview findings, we categorized responses into three core challenges: (1) self-efficacy, (2) education, and (3) engagement with the primary caregiver. Further details are given below.  
1. Self-efficacy: Issues related to self-efficacy resulted from patients having an unclear or no indication of progress and improvement during rehabilitation. Patients commonly stated that they often questioned the effectiveness of their therapy and had limited engagement with the exercises they were performing. There was an overall interest in being able to visualize the effects their therapy was having on their recovery and be able to track progress at each stage of rehabilitation. One interviewee stated, \"It is easy to notice the pain, but I cannot see the improvement or progress\".
2. Education: All interviewees stated that they were issued a paper-based treatment program detailing a summary of their injury and rehabilitation procedure. Users reported limited engagement with this information, reporting that the initial exercise demonstrations provided by their caregivers were more effective in reinforcing the correct exercise technique than static images. One interviewee noted that YouTube videos on exercises were beneficial. Overall, the key takeaway from this was that patients responded better to information that was presented in an engaging manner. Subsequent design concepts must explore ways that this can be achieved. One interviewee stated, \"I had no level of engagement with my exercises... I did not even know if what I was doing was correct.\"
3. Engagement with the primary caregiver: There was a shared view amongst users that they felt they had just been left to their own devices once discharged from the ward. Feedback would often be provided during patient follow-up; however, this was commonly described as either being'monotonous', 'disengaging' or 'useless.' One interviewee remarked that something as simple as being told she was making small improvements by her caregiver would have been enough to motivate her to be persistent with therapy. In addition, one interviewee stated, \"It would have been nice to have a follow-up with a healthcare specialist who understood the challenges I was having and gave useful advice. The feedback I was provided was often always the same\".
# Adaptive Frame Sampling and Feature Alignment for Multi-Frame Infrared Small Target Detection
## 4 Experiments  
### Datasets  
This paper used the real low-altitude scene infrared small target dataset [45] published by the National University of Defense Technology for training and testing the network. The typical wavelength range of this dataset is 3-5 um, which belongs to the medium-wave infrared. There are 22 typical scenes, which mainly include a single target with a low-altitude complex background, targets moving from far to near, targets moving from near to far, the target leaving the field of view, the target returning to the field of view, etc. Most of the scenes in the dataset are ground backgrounds, including forests, rivers, buildings, etc. Each typical scene corresponds to a data sequence, with a total of 22 data sequences, 16,177 image frames, and 16,944 targets with labels. Each infrared image has a resolution of 256 \(\times\) 256 pixels and a bit depth of 8 bits. Typical small target samples from the dataset are shown in Figure 7. The environmental background of the dataset and the signal-to-clutter ratio (SCR) information of the data are shown in Table 2. The definition of SCR is as follows:\[SCR=\frac{\left|\gamma_{tg-\gamma_{img}}\right|}{\sigma_{img}} \tag{14}\]  
where \(\gamma_{tg}\) is the brightness of the target area, \(\gamma_{img}\) is the average brightness of the image, and \(\sigma_{img}\) is the standard deviation of the image brightness.
# State-of-the-Art Survey on Deep Learning-Based Recommender Systems for E-Learning
## 3 Classification Method
### Classification Based on Recommendation Techniques
#### 3.1.3 Knowledge-Based Technique  
The two techniques discussed above require a reasonable volume of data representing users' previous purchasing history and rating experiences, making them unsuitable to the domain where items are highly customized. Such customized products include automobiles, financial services, real estate, tourism guide systems, and so on, rarely purchased in large quantities, as such ratings are not sufficiently available. The knowledge-based technique does not require any rating correlation or user profile to make predictions, making it convenient to recommend items not regularly purchased [22]. Knowledge-based RSs use item attributes, user specifications, and domain knowledge to generate recommendations. Instead of depending on users' ratings, a knowledge-based system only needs information about users' preferences, such as why they are looking for that item based on a lower price, high quality, or luxury. The system requires the information to make suitable recommendations by considering items that satisfy users' requirements.
# Epileptic Seizure Detection in Neonatal EEG Using a Multi-Band Graph Neural Network Model
## 3 Methods
### Graph Generation
#### 3.2.2 Graph Edge Generation  
The patterns of neural synchronization are considered to be an important indicator of consciousness function and dysfunction [25; 26]. In this paper, the synchronization is calculated as the connection strength between different channels. Thus, a weighted graph can be constructed based on the connection matrix by considering each channel as a node. The PLV is a commonly used bivariate measurement that reflects the degree of phase synchronization in a particular frequency band.  
Given that \(x(t)\) is the input time series signal, the Hilbert transform (HT) of \(x(t)\) is defined as follows:  
\[\hat{x}(t)=H(x(t))=\frac{1}{\pi}P\int_{-\infty}^{\infty}\frac{x(\tau)}{t-\tau}d\tau \tag{3}\]  
where \(P\) denotes the Cauchy principal value [27]. The analytic representation of \(x(t)\) can be written as follows:  
\[\Psi(t)=x(t)+i\hat{x}(t)=A(t)e^{i\phi(t)} \tag{4}\]where \(A\) is the amplitude term and \(e^{i\phi(t)}\) is the exponential term. \(\phi(t)\) is the instantaneous phase of \(x(t)\) at time point \(t\), calculated by the following:  
\[\phi(t)=\arctan\biggl{(}\frac{\hat{x}(t)}{x(t)}\biggr{)} \tag{5}\]  
Given that \(\phi_{x}(t)\) and \(\phi_{y}(t)\) are the instantaneous phase of the input signal \(x(t)\) and \(y(t)\) at time point \(t\), respectively, the instantaneous phase difference could be calculated by the following:  
\[\triangle\phi_{x,y}(t)=w_{1}\phi_{x}(t)-w_{2}\phi_{y}(t) \tag{6}\]  
where \(w_{1}\) and \(w_{2}\) are the weights of phases which are both set to \"1\" here. Consequently, the PLV of the input signal \(x(t)\) and \(y(t)\) is the average of phase differences in a time window, calculated by the following:  
\[PLV=\frac{1}{N}\left|\sum_{n=0}^{N-1}e^{i\triangle\phi_{x,y}(n)}\right| \tag{7}\]  
where \(N\) is the window length and \(n\) is the time index in the window. The PLVs range from \"0\" to \"1\", where \"0\" means two signals are completely asynchronized and \"1\" means two signals are fully synchronized.
# FFT-Based Scan-Matching for SLAM Applications with Low-Cost Laser Range Finders
## 2 Modeling, Data Pretreatment, and Missing Data Features  
### Robot Modeling  
In our work, a two-wheel driven robot moving on an indoor flat plane was studied, as shown in Figure 1. The scan-matching problem here is defined as finding the transformation between two robot poses based on the scan data acquired on each pose. Two coordinate systems are established here, marked as model coordinate system (\(X_{M}O_{M}Y_{M}\)) and scene coordinate system (\(X_{S}O_{S}Y_{S}\)). The transformation parameters are X, Y, and \(\alpha\), where X and Y are the translation parameters between the two poses and \(\alpha\) is the rotation angle.  
The rotation matrix \(R\) and translation matrix \(T\) can be used to represent the transformation between the two poses:  
\[R=\left[\begin{array}{cc}\cos\alpha&-\sin\alpha\\ \sin\alpha&\cos\alpha\end{array}\right] \tag{1}\]  
\[T=\left[\begin{array}{c}X\\ Y\end{array}\right] \tag{2}\]
## 6 Simulated Results
Among them, the MCSO with the initial tracking voltage fixed at 0.8 \(V_{\rm mp}\) combined with pace adjusted with the slope of the P-V curve and inertia weight provided optimal tracking speed response and steady performance. Figure 16: P-V characteristic curve simulation for case 6. Figure 15: Simulation results for case 5: MPPT with conventional CSO and four modified methods under solar irradiance change from 1000 to 500 W/m\({}^{2}\) and back to 1000 W/m\({}^{2}\). In cases 4, 5, and 6, the conventional cat swarm optimization (CSO) method failed to locate the global maximum point. This is because the maximum number of iterations for each tracking method was set to 30 in this study. As a result, the three cases using conventional cat swarm optimization were unable to track the global maximum power point within the limit of 30 iterations. However, as the solar exposure decreased from 1000 W/m\({}^{2}\) to 500 W/m\({}^{2}\), the maximum power point voltage did not change significantly because the shading conditions did not change. The previous tracking did not capture the global maximum power point, but it was close. As a result, tracking the global power point under 500 W/m\({}^{2}\) solar exposure took only a few iterations. Tables 7 and 8 show comparisons of tracking time and oscillation amplitude when performing maximum power point tracking at the beginning of tracking for the six test cases using conventional cat swarm optimization (CSO) and four types of modified cat swarm optimization (MCSO). The tables show that, under various test conditions, the CSO with fixed initial tracking voltage combined with tracking pace adjusted by the slope of the P-V curve and inertia weight not only tracked the global maximum power point faster but also had a lower oscillation amplitude. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & & **Tracking Speed** & & \\ \cline{3-6} \multirow{2}{*}{**Case**} & \multirow{2}{*}{**Number of Peaks on**} & \multirow{2}{*}{**Conventional**} & \multirow{2}{*}{**CSO with Fixed**} & \multirow{2}{*}{**CSO with Fixed**} & \multirow{2}{*}{**CSO with Fixed**} & \multirow{2}{*}{**CSO with Fixed**} \\  & & & & **Initial Tracking** & & **Initial Tracking** \\  & & & **Voltage** & & **Voltage Combined** & & **Voltage Combined with** \\  & & & & **Voltage** & & **Collapsed with** \\  & & & & **Voltage** & & **Traching Pace** & & **Adjusted by Slope** \\  & & & & **Slope of P-V Curve** & & & **Heritia Weight** \\ \hline
1 & Single peak & 0.077 s & 0.05 s & 0.048 s & 0.042 s & 0.038 s \\
2 & Double peak (MPP & 0.061 s & 0.051 s & 0.049 s & 0.003 s & 0.028 s \\  & at right) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Tracking speed comparison of six cases. Figure 17: Simulation results for case 6: MPPT with conventional CSO and four modified methods under solar irradiance change from 1000 to 500 W/m\({}^{2}\) and back to 1000 W/m\({}^{2}\).
###### Abstract
When we say it is latency-aware, we mean that AIMSLA adapts the control of the AVs to the inherent latency of the 5G network, thus providing safety and smooth traffic flow. If communication latency were not considered, a small delay in control could cause a vehicle to receive an order later than desired, leading to undesirable situations, including collisions. Although AIMSLA is derived from an AIM system implemented in previous work [4], AIMSLA has several improvements. These enhancements are:  
1. Taking into account the previous latencies of all AVs using a deep neural network based on a recurrent neural network to encode the latencies of the other AVs;
2. The network is trained on a 5G communication network  
* Predicting the latency that each AV is going to experience in the next timestep, using a Transformer-based deep neural network, to adapt the control of the AVs;
* Consider the number of AVs to be controlled simultaneously as an internal parameter to adapt the control according to the number of AVs;
* Propose the set of messages as well as the communication protocol for AIM5 LA implementation. The rest of the article remains as follows: In Section 2, we present the related work on AIM and focus especially on latency-aware AIMs. In Section 3, we present the proposed AIM5 LA mechanism as well as the necessary message protocol. The simulations performed as well as the testbed used are described in Section 4. In Section 5, the results obtained are analyzed and discussed. Finally, Section 6 concludes and proposes future work.
# Follow-Up Control and Image Recognition of Neck Level for Standard Metal Gauge
## 3 Image Segmentation Algorithm Based on Edge Detection and K-Means Clustering Algorithm
### Filtering of Images  
A filtering step was included to suppress noise in the original images and preserve details. This involved the use of a smoothing kernel and a low-frequency enhanced spatial domain filtering technique. Some operations, such as low-pass filtering, can blur edge features. However, this application required detecting edges without losing feature information. Special classes of filters exist to simultaneously optimize these two objectives, including anisotropic diffusion and bilateral filtering, the effects of which are shown for the original image [11]. The results produced by these two algorithms are visually similar. Quantitative assessment was also performed using peak signal-to-noise ratio [12] (PSNR) and the structural similarity index [13] (SSIM).  
PSNR is based on the average calculation of gray values for image pixels. It is a basic quality evaluation algorithm and a commonly used indicator for measuring signal distortion. PSNR compares the original and distorted images on a pixel-by-pixel basis, calculates the total (summed) error between the pixels, and provides an evaluation score. Its value is typically in the range of 20-50, with larger values representing higher image quality. SSIM compares the brightness, contrast, and structural similarity of the original and processed images, returning a value in the range of 0-1. Larger values indicate better image quality, with unity indicating identical data. PSNR and SSIM values for the liquid level images are shown in Table 1. An analysis of these results suggests that bilateral filtering is superior to anisotropic diffusion, as measured by both the PSNR and SSIM. Bilateral filtering considers both proximity in the spatial domain and similarity in grayscale and brightness. As such, it is independent of anisotropic diffusion filtering. Processing results were also more effective because of connections between pixels. As such, bilateral filtering was selected for use in this study.
## 4 Results and Discussion
### Characterisation of Membranes
#### 4.1.3 Static BSA Adhesion Resistance  
The BSA adhesion performance of each membrane is shown in Table 2. The UV absorption percentage indicates the amount of BSA in the solution after a 3 h incubation period. The larger value refers to less BSA adhesion, and also to higher BSA adhesion resistance. The membranes modified by TiO\({}_{2}\) using both binding methods adsorb less BSA in comparison to the neat TFC membrane and the membrane modified by polydopamine.  
This observation is in agreement with the fact that more hydrophilic surfaces adhere less protein under the same adsorption conditions [11; 37]. However, the hydrophilicity does not seem to be the only factor that can influence the protein adhesion; the surface structures of the membranes and the functional groups also contribute to the protein adhesion ability. For example, the membrane modified by TiO\({}_{2}\) using polydopamine binding is less hydrophilic than the membrane modified by self-assembled TiO\({}_{2}\). This less hydrophilic surface does not lead to more protein adhesion. Instead,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Membranes** & **Neat** & **Neat + PDA** & **Neat + pDA + TiO\({}_{2}\)** & **Neat + TiO\({}_{2}\)** \\ \hline Contact angle & 50.4 \(\pm\) 2.3 & 59.3 \(\pm\) 1.3 & 27.2 \(\pm\) 2.9 & 24.5 \(\pm\) 2.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Water contact angle for the unmodified and modified membranes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Solutions** & **Original BSA Solution** & **Neat** & **Neat + pDA** & **Neat + pDA + TiO\({}_{2}\)** & **Neat + TiO\({}_{2}\)** \\ \hline UV absorption percentage (\%) & 32.4 \(\pm\) 0.7 & 19.3 \(\pm\) 1.0 & 21.6 \(\pm\) 1.2 & 29.7 \(\pm\) 1.0 & 26.7 \(\pm\) 1.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: UV (ultraviolet) absorption percentage of the BSA (bovine serum albumin) solution after a 3 h incubation period.  
it shows the best protein adhesion resistance. This is probably due to the relatively regular nanoscale papillae structure and the uniform hydrophilicity throughout the membrane surface. Although the surface roughness of the polydopamine-modified membrane is higher than the other membranes, the protein molecules cannot easily attach on the membrane surfaces. As suggested by [PERSON] et al. [38], the pore size of the polydopamine-modified surfaces is around 1.5 nm. The dimension of the prolate ellipsoid BSA is 14 nm for the major axis and 4 nm for the minor axis [39], which is significantly larger than the pore size of the polydopamine. Due to the larger size, BSA cannot settle into the pores of the polydopamine. In addition, the hydrolyzed surface functional groups (i.e., hydroxyl, quinone, and secondary amine groups), can repel the protein molecules, preventing them from attaching. These properties of the polydopamine layer lead to a better protein adhesion resistance [37].
# A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems
## 3 Taxonomy of Cyber-Physical Attacks
### Physical System Layer
#### 3.1.2 Controller Attack  
A controller attack is defined as a modification of the control input signal \(u(t)\) on a network, which is represented as the addition of the controller attack signal to the legitimate control input signal, as follows:  
\[\dot{\tilde{x}}(t)=A\tilde{x}(t)+B(u(t)+u^{a}(t)), \tag{4}\]  
where \(\tilde{x}(t)\) is the attacked state of the physical system, and \(u^{a}(t)\) is the control attack signal from the malicious user. The purpose of a controller attack is to destabilize the physical state \(x(t)\) by injecting an unexpected control input signal \(u^{a}(t)\). When a controller attack is launched, the physical system does not operate as intended by the computing system, because both the control input signal \(u(t)\) from the computing system and controller attack signal \(u^{a}(t)\) influence the physical dynamics, \(x(t)\).  
There are some advanced controller attacks used to avoid detection by conventional model-based detectors. In particular, sophisticated controller attacks exploit zero-dynamics [59], which is a specific behavior of the sensor measurement \(y(t)=0\) related to system dynamics. Attacks on the control input signal \(u(t)\) and initial physical state \(x(0)\) are reported in various cyber-physical security studies as the so-called zero-dynamics attack (ZDA) [60]. The ZDA targets the unstable zero-dynamics inherent in the physical system [60] or unstable zero-dynamics generated by discretization [61; 62]. When a ZDA is launched, internal physical state \(x(t)\) diverges to infinity; however, this divergence is not revealed in sensor measurement \(y(t)\) due to a characteristic of zero-dynamics.
# Automatic Microscopy Analysis with Transfer Learning for Classification of Human Sperm
## 2 Materials and Method
### Data Preprocessing  
In deep learning sector, to make the models learn the characteristics of the data better, dataset is commonly preprocessed before being feed into the algorithms [26; 27]. Instead of enhancing the dataset by enlarging the number of training images, a more concise and effective data preprocessing method is adopted here. Due to the limited amount of available data in HuSHeM dataset, to make full and effective use of each sample, the sperm heads in the images were cropped and rotated to the same direction before being fed into the network. To boost the image preprocessing efficiency, a specific program based on OpenCV was developed to automatically crop and rotate the images in batches.  
The flow diagram of the image preprocessing method is shown in Figure 3. The original image (Figure 3a) was first denoised and converted to monochrome image. The Sobel operator was then applied to obtain the gradient image with high horizontal gradient and low vertical gradient, as shown in Figure 3b. Subsequently a low-pass filter was employed to remove the high-frequency noise in the gradient image, and the resulting image was binarized using an adaptive thresholding algorithm. To find out the contour of the sperm head, the binary image was morphologically eroded and dilated to eliminate interference spots (Figure 3c). Then, the contour of the sperm head was processed by elliptical fitting to obtain the major and minor axis (Figure 3d). Finally, the main feature area centered on the ellipse, namely the head of the sperm (Figure 3e), was cropped. As a result, a dataset consists of images with the size of \(64\times 64\) pixels containing valid information of about the sperm head was obtained, as shown in Figure 2b.  
In order for the algorithm to learn the characteristics of different types of sperm precisely, we also investigated the influence of the head orientations of the sperm. In the experiments, the head of the sperm was aligned to the uniform direction (i.e., pointing to the right as shown in Figure 3f. The rotated image was further cropped with the same image size (See Figure 3g). The example images of the acquired dataset are shown in Figure 2c. The performance of the algorithm on the three distinct datasets (i.e., Figure 2a-c) is compared in the Section 3.  
Figure 2: The processing of the dataset. (**a**) The original HuSHeM dataset. (**b**) The dataset composed of cropped images. (**c**) The dataset composed of aligned and cropped images.