esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 First Medal Prediction Model (FMPM)
### Prediction Application and Conclusion  
The trained neural network model is deployed in the medal prediction process for the 2028 Olympics, based on the prediction data of 78 countries that have never won a medal. The ARIMA model is used to generate the prediction of the number of participants. For countries with a cumulative number of participations of less than three, the number of participants is set to the value of their most recent Olympic Games by default. The prediction results are shown in Figure 14.  
According to Figure 14, among the 78 countries that have never won a medal, only two countries (UAR and MLI) have a medal-winning probability of over 50% in the 2028 Olympics, which are 77.47% for UAR and 58.47% for MLI, respectively. Specifically, UAR is expected to send 26 athletes, with the country having a total of 3 participations; MLI is expected to send 25 athletes, with the country's having a total of 14 participations. The  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Predicted 0** & **Predicted 1** \\ \hline Actual 0 & 352 & 40 \\ Actual 1 & 60 & 236 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrix.  
Figure 14: FCNN prediction in 2028.  
relatively high medal-winning probabilities of these two countries may be related to their stable number of participants and gradually increasing participation experience.  
In addition, six countries have a medal-winning probability of over 30%, including Guatem (48.50%) and Senegal (45.70%), showing a certain potential to win medals. However, more than 85% of the countries have a medal-winning probability of less than 40%. Among them, the countries with the lowest probabilities (UHN and UNK, approximately 3.16%) plan to send only one athlete or not participate at all, which reflects the limitations of the insufficient participation scale and experience on the possibility of winning medals.
## 5 Simulation  
In this section, we construct a simulation platform to verify the proposed DP-SIMLE algorithm. Under this controlled environment, we simulate the signals received by a dual-polarization antenna such that we could: (1) exclude any other error sources except multipath error; (2) specify the received power of the LOS and MP signals for both RHCP and LHCP antennas; and (3) control the relative delay of the multipath signals to investigate the DP-SIMLE algorithm thoroughly. We firstly use this platform to simulate the one LOS and one reflected path environment to verify the theoretical analysis. Secondly, we investigate how the \(DPG\) and the power of the LOS signal received by the LHCP antenna affect the performance of DP-SIMLE. Thirdly, we simulate the critical cases when the LHCP channel provides invalid information about the multipath. Finally, we simulate a one LOS with three reflected path environment to test the DP-SIMLE algorithm in the severe multipath environment.
## 2 Literature Review
### Network Architecture Search in the Field of ML  
Designing an ML model suitable for specific application scenarios requires expertise, extensive knowledge in the professional field, and considerable time to experiment [31]. In particular, the architecture is defined as the components constituting the model and its hyperparameters [32; 33]. To optimize the model's architectural performance effectively, researchers have proposed the network architecture search (NAS) theory [34]. This search strategy determines a model architecture offering the best performance using the search strategy \(\pi(\cdot)\) based on the model's performance-evaluation index (\(L\)) in a network architecture search space (\(\mathbf{\mathcal{A}}\)) [35]. Assuming that \(\mathbf{D}=\{(\mathbf{X},Y)|\mathbf{X},\mathbf{Y}\in\mathbf{R}^{n}\}\) represents the dataset used for model training, \(\mathbf{A}\in\mathbf{\mathcal{A}}\) represents a certain architecture of the model, and \(w\) represents the weights in the model, the general form of NAS is expressed in Equations (1) and (2):  
\[\min_{A\sim\pi(A)}L[\mathbf{A}(w_{A}),\mathbf{D}_{\text{valid}}], \tag{1}\]  
\[\text{s.t.}w_{A}=\text{arg}\min_{w}L[\mathbf{A}(\mathbf{w}),\mathbf{D}_{\text{train}}], \tag{2}\]  
where \(\mathbf{D}_{\text{valid}}\) and \(\mathbf{D}_{\text{train}}\) represent the randomly separated validation and training datasets obtained via \(\mathbf{D}\), respectively. Common search strategies used in NAS problems include evolutionary algorithms (EA) [35], reinforcement learning (RL) [36; 37], and gradient optimization (GO) [32]. Although EA-based and RL-based NAS methods can reduce manual intervention and obtain better-performing model architectures, these methods require computational resources that typically run on thousands of graphical processing unit (GPU) days [34]. To solve these problems, the GO-based NAS method, represented by DARTS [38], has gradually emerged as a solution. DARTS utilizes a relaxation method to transform the search space from discrete to continuous and uses a gradient-based method for optimization, and eliminates the need for extra components such as Controller [36] or Hypernet [39], thereby reducing the modeling difficulty and improving the optimization efficiency.
## 2 Hybrid Graphene-Based Materials (Hybrid GBMs)
### Graphene-Based Biosorbents
#### 2.5.2 Graphene-Based Materials Immobilized with Cyclodextrin
Subsequently, the PDMS/GO/\(\beta\)-CD composite was obtained by functionalizing the PDMS/GO sponge with \(\beta\)-cyclodextrin(in, after which it was allowed to dry at 60 \({}^{\circ}\)C to achieve the final NS sorbent. The results revealed a hierarchical porous interconnected morphology featuring continuous macro-sized cavities, enabling the material to be utilized in six sorption-desorption cycles while maintaining satisfactory analytical performance. While CS and CD are widely recognized as two of the most commonly utilized biopolymers for graphene derivative modification, the literature also highlights alternative methodsfor crafting graphene-based biosorbents, particularly for application in microextraction procedures. For instance, cellulose has proven to be an excellent material for modification with graphene-based sorbents. Recently, [PERSON] et al. [77] described the synthesis of a reduced graphene oxide/cellulose nanocrystal (rGO/CNC) composite for microextraction applications. The biosorbent was produced by mixing GO and cellulose in a reaction medium, followed by a hydrothermal reaction and a freeze-drying process. Furthermore, agarose [41] and alginate [78] biopolymers have emerged as viable candidates for crafting graphene-based materials, showcasing the adaptability of graphene in manifesting diverse modification potentials with these natural materials. CS and CD have found widespread application in conjunction with GBMs, leveraging their unique attributes such as remarkable supramolecular recognition and facile functionalization. Indeed, the exploration extends beyond CS and CD; various other natural materials have been investigated, broadening the spectrum of synthesis approaches and material choices for diverse applications. Furthermore, it is equally important to note that utilizing biosorbents based on GBMs represents a significant advancement towards environmentally conscious practices in analytical chemistry. Due to their inherent biodegradability, synthesis procedures employing GBMs with biomaterials are environmentally friendlier than traditional methods. As a result, this approach protects the environment from chemical waste production and ensures the well-being of researchers interested in applying such materials.
# Exploiting Frequency Characteristics for Boosting the Invisibility of Adversarial Attacks
## 3 Methodology
### Attack Algorithm  
Based on the above Fourier domain analysis for adversarial attacks, we propose an invisible transferable adversarial attack in this subsection. Several studies from a model-based perspective have argued that the decision boundary and the model architecture of a substitute model all have a significant impact on adversarial transferability [34]. Since the possible target model is completely unknown, the use of model augmentation to reduce the dependence on the decision boundary of the substitute model is a common strategy for transferable adversarial attacks [1; 2; 4]. Here, we use frequency domain model augmentation [1] and combine it with the Fourier sensitivity analysis of DNNs to make the augmentation more directed. Specifically, we transform the model input to the Fourier domain and apply noise addition and enhancement to redirect the perturbation toward the common frequency-sensitive regions of DNNs. The augmentation can be formulated as follows:  
\[\mathbf{x}_{\text{aug}}=\text{IFFT}(\mathbf{x}_{\text{FFT}}\odot\mu+\xi) \tag{5}\]  
where \(\odot\) represents the Hadamard product, the multiplicative noise \(\mu\) is uniform noise sampled from a uniform distribution, that is, \(\mu\sim\text{U}(1-\rho,1+\rho)\), and the additive noise \(\xi\) is set to Gaussian noise \(\xi\sim N\big{(}0,\sigma^{2}\big{)}\). Specifically, we add frequency domain noise to the model input in the Fourier domain, improving the transferability of adversarial examples. Next, we quantitatively analyze the impact of different frequency components of perturbations on the transferability and invisibility of adversarial examples. We employ a clustering analysis to select the most valuable regions in the Fourier domain for adversarial attacks, which guide the adjustment direction of the frequency domain distribution of the perturbation. We propose a frequency domain loss, using it alongside adversarial loss, and we use the cross-entropy loss here as the joint loss function to derive and design the perturbation from the Fourier domain. We propose to combine the frequency domain robustness of DNNs and the frequency characteristics of the HVS to perform the adversarial attack from the frequency domain and enhance the invisibility of the transferable adversarial examples. The details are given in Algorithm 1. As shown in Algorithm 1, our method can be divided into three stages. Firstly, we transfer the input image to the frequency domain and perform frequency domain augmentation. Then, we take back-propagation of the joint loss function in the Fourier domain and update the adversarial example by applying a frequency domain step size of \(\alpha\) while ensuring that the pixel values are in the normal range by normalization.
# Novel Hand Gesture Alert System
## 1 Introduction  
In a sexual assault, the assailant assaults the victim quickly and brutally, without any prior contact, usually at night in a public place. This can be done by physical force or threats of force, or by the abuser giving the victim alcohol as part of the crime. Sexual assault includes rape and sexual coercion [1]. In the United States, a significant number of women face sexual assault every day, and about one in three women have been victims of this crime [2]. Similarly, the number of sexual assaults in Europe increased between 2008 and 2016 [3] despite the tremendous increase in security tools such as cameras, which require special human attention to analyze the scene [4; 5; 6]. Most of the studies proposed in the past few years tend to answer the question: was it a sexual assault? However, few of them focus on the early detection of this crime [7; 8]. Indeed, this is a difficult task, as the posture of both individuals (the rapist and the victim) might be both on the same abscissa of the camera, as shown in Figure 1. Regardless of the victim's position, a part of his/her body may be visible most of the time, especially the hands, which may describe a specific pattern if the victim is aware of what is known as the \"security-gestures\" described in this article. This article aims to present a new hand gesturealert system, which takes advantage of a defined set of gestures that can trigger a warning when the described computer vision system detects them.  
To achieve this aim, the system uses human detection, hand extraction, and Convolution Neural Networks (CNNs) [9; 10; 11; 12]. It detects human bodies in a video, extracts the region of interest (hands) and detects the hand gesture that will be processed and will trigger the alert, if the hand gesture corresponds to one of the predefined hand gestures. The remaining part of this article is structured as follows: first of all, the related work will be presented; secondly, the proposed architecture will be addressed; thirdly, experiments, results, and discussion will be presented; lastly, a summary of the proposed work and further perspectives are offered in the conclusion.
# Pixel Circuit Designs for Active Matrix Displays
## 3 OLED Pixel Circuits
### OLED Novel Pixel Circuits
#### 3.2.5 Silicon-Based OLED Pixel Circuits  
For silicon-based micro-OLED displays, the pixels occupy only micro-square meters due to densities exceeding 3000 pixels per inch. The current range of micro-OLED is very small, ranging from only a few hundred pico-amps to a few tens of nano-amps. A small current can alleviate the lifetime problem of OLED at high brightness and high current density [91], but whether its lifetime hours are sufficient for some applications still needs to be determined on a case-by-case basis. Notably, the \(V_{\text{GS}}\) of the transistors in the pixel circuit needs to be regulated within a narrow range in the subthreshold region. Current grows exponentially, and \(V_{\text{TH}}\) needs to be compensated for even with complementary metal oxide semiconductors (MOSFETs) exhibiting small \(V_{\text{TH}}\) variations [90].  
The SF method usually involves using the source of a MOSFET as the output and the gate as the input. This method is used to ensure that the voltage signal follows the input signal and to reduce voltage distortion. The CD method controls the voltage by dividing the voltage signal across two capacitors, using the voltage division principle of the capacitors. To solve the problem of narrow \(V_{\text{DATA}}\) range problem, source follower (SF) and capacitive division (CD) methods have been proposed for silicon-based pixel compensation circuits (Figure 8). The SF utilises OLED or diode-connected transistors as source loads to extend the \(V_{\text{DATA}}\) range from the gate node to the source node [92,93]. CD utilises a metal-insulator-metal (MIM) integrated capacitor and parasitic gate capacitance of the drive transistor, which are two capacitors, to extend the \(V_{\text{DATA}}\) range [94,95].  
Compared to TFTs, variations in the source bulk voltage (\(V_{\text{SB}}\)) of MOSFETs cause variations in \(V_{\text{TH}}\), resulting in a bulk effect problem. However, the SF and CD methods do not solve this problem, and their driver transistors have different source voltage levels in the compensation and emission periods, resulting in increased current errors. [PERSON] and [PERSON] proposed a pixel circuit consisting of MOSFETs and capacitors that is not affected by the bulk effect (Figure 7f) [90]. This circuit employs capacitors C1 and C2 to expand \(V_{\text{DATA}}\) to a range that a source-driven conventional analogue-to-digital conversion can accommodate. It connects the driver transistor's source and gate to the supply voltage(\(V_{\rm DD}\)), ensuring that the driver transistor's \(V_{\rm TH}\) remains unaffected by the bulk effect. Additionally, M4 facilitates the connection of a diode to the driver transistor M6 for \(V_{\rm TH}\) compensation. This enables extended \(V_{\rm DATA}\), independence from the bulk effect, and \(V_{\rm TH}\) compensation functions. But, this adds two lines that limit the display resolution.
## 3 Materials and Methods
### Background
#### 3.1.5 Performance Metrics  
In the realm of machine learning, true positives (\(TP\)) refer to the number of correctly predicted positives. True negatives (\(TN\)) refer to the number of correctly predicted negatives. False positives (\(FP\)) refer to the number of incorrectly predicted positives. False negatives (\(FN\)) refer to the number of incorrectly predicted negatives. To evaluate the performance of each algorithm, we used accuracy, precision, recall, and F1 scores.  
1. Accuracy measurement is used to measure the overall correctness of the model. It is defined as the ratio of correct predictions to total observations, as shown in Equation (5). It is especially useful when the classes are well-balanced. \[Accuracy=\frac{(TP+TN)}{(TP+TN+FP+FN)}\] (5)Precision measures the accuracy of the positive predictions by evaluating the ratio of true positives to total predicted positives; it is mathematically defined in Equation (6).  
\[Precision=\frac{TP}{(TP+FP)} \tag{6}\]  
Recall is used to measure the ratio of true positives to the actual positives (# of true positives + # of false negatives), as shown in Equation (7). It answers the following question: \"How many did the model correctly identify from the set of all actual positives?\". It is crucial in situations where the cost of false negatives is high.  
\[Recall=\frac{TP}{(TP+FN)} \tag{7}\]  
The F1 score is the harmonic mean of precision and recall. It presents a way to combine both parameters into a single measure. If there is an uneven class distribution, the F1 score helps to strike a balance between the two parameters, as shown in Equation (8).  
\[F_{1}\;\text{Score}=2\cdot\frac{(\text{Precision}\cdot\text{Recall})}{\text{ Precision}+\text{Recall}} \tag{8}\]  
This study focuses on training supervised classifiers on multi-sensor data from a cloud-based condition monitoring system to track industrial rotating machines. This highly reliable system collects vibration, temperature, and ultrasound data from multiple sensors, enabling real-time analysis and early fault detection. It adapts to varying operating conditions, allowing the continuous monitoring of machine performance. The data collection procedure and the particular parameters used are explained in the following sections.
## 3 Results and Discussion
### Stress-Strain Curve
Consequently, the inclusion of BF is beneficial for reducing the initial internal defects of coal-gangue concrete, with the inherently high stiffness of BF resulting in an increased elastic modulus. Conversely, with PF, characterized by weak bonding with the coal-gangue concrete matrix and by low stiffness, the elastic modulus of gangue concrete decreases. However, an excessive mixture of BF and PF results in an increase in internal defects within coal-gangue concrete, subsequently reducing the elastic modulus. Relative to the benchmark coal-gangue concrete, HBPRGC-20-0.2 and HBPRGC-30-0.2 experience reductions of 22.81% and 26.78% in elastic modulus, respectively. The addition of fibres can markedly improve the ductile deformation of coal-gangue concrete. When either BF or PF is added individually, BF exhibits a greater influence on the strength of coal-gangue concrete than PF, and PF enhances the ductile deformation slightly more than BF. The combination of BF and PF has the most pronounced effect on coal-gangue concrete ductile deformation among the tested specimens. At a 0.2% hybrid-fibre volume content, the ductile deformation of gangue concrete increases significantly. However, the strength is lower than the situation in which the hybrid-fibre volume content is 0.1%. Therefore, 0.1% seems to be the optimum hybrid-fibre volume content for enhancing both the ductility and strength of coal-gangue concrete.
# A Method for Detecting the Yarm Roll's Margin Based on VGG-UNet
## 3 Materials and Methods
### Dataset Construction
#### 3.2.3 Extraction of Yarn Margin Contour Points  
To determine the fitting circle of the end face, the process begins by employing the Canny edge detection algorithm to extract edge contour points from the bobbin region mask image. Initially, the Sobel operator is used to calculate the gradient intensity (\(G\)) and direction (\(\theta\)) for each pixel in the image. Following this, non-maximum suppression is utilized to remove non-edge pixels, resulting in refined edge points. The specific calculation formula for these steps is outlined below.  
\[G=\sqrt{G_{x}^{2}+G_{y}^{2}} \tag{1}\]  
\[\theta=\arctan\left(\frac{G_{x}}{G_{y}}\right) \tag{2}\]
# Real-Time Sea State Estimation for Wave Energy Converter Control via Machine Learning
## 5 Result Analysis
### Variable Sea State: Prediction and Control  
The water surface elevation function \(\eta(t)\) is constructed as  
\[\eta(t)=\sum_{i=1}^{N}\sqrt{2S_{c}(\omega_{i},t)}\sin(\omega_{i}t+\phi_{i}) \tag{27}\]  
where \(\omega_{i}\) is the angular frequency, \(\phi_{i}\) is a random phase uniformly distributed in \([0,2\pi]\), and \(S_{c}(\omega,t)\) is the time-dependent wave spectrum transitioning between two sea states:  
\[S_{c}(\omega,t)=\frac{5\pi^{4}H_{s}(t)^{2}}{T_{p}(t)^{4}\omega^{5}}\exp\left( \frac{-20\pi^{4}}{T_{p}(t)^{4}\omega^{4}}\right) \tag{28}\]  
\[H_{s}(t)=m_{1}(t)H_{s1}+m_{2}(t)H_{s2}, \tag{29}\]  
\[T_{p}(t)=m_{1}(t)T_{p1}+m_{2}(t)T_{p2}, \tag{30}\]  
where the time-dependent mixing coefficients are given by  
\[m_{1}(t)=1-\frac{t}{t_{\rm end}},\quad m_{2}(t)=\frac{t}{t_{\rm end}}. \tag{31}\]  
This ensures a smooth transition from sea state 1 at \(t=0\) to sea state 2 at \(t=t_{\rm end}\). The resulting wave elevation function \(\eta(t)\) accurately represents a dynamically evolving sea state.  
Figure 8 presents the WEC motion response under a dynamically varying wave period \(T_{p}\) ranging from 2 s to 6 s. The left column displays the control parameters: the estimated period \(T_{p}\), proportional gain \(K_{P}\), and integral gain \(K_{I}\), while the right column illustrates the WaveBot device response in terms of position, velocity, and PTO force over time. In the first column, it is evident that the predicted period approach is tracking the changing sea state and adjusting the proportional and integral gains accordingly.  
Figure 9 illustrates the average power capture over time for two control approaches: a fixed \(T_{p}\) of 4 s (conventional method) and a dynamically predicted \(T_{p}\). Unlike the fixed approach, where \(T_{p}\) remains constant, the proposed method adaptively updates \(T_{p}\) based on real-time responses from the WaveBot device, enabling the continuous tuning of the proportional gain \(K_{P}\) and integral gain \(K_{I}\). The results demonstrate a significant improvement in power capture with the adaptive approach. The proposed method achieves an average power of 34.09 W, compared to 27.13 W with the fixed \(T_{p}\). This demonstrates a 25.6% increase in power capture compared to the fixed \(T_{p}\) approach. This increase in power extraction efficiency is attributed to the real-time adjustment of control parameters, which allows the WEC to better synchronize with incoming wave conditions. By dynamically optimizing \(K_{P}\) and \(K_{I}\), the system effectively maximizes energy conversion, validating the advantage of adaptive control over traditional fixed-gain strategies.
## 2 Data and Methods
### Data Sources  
In this study, we conducted a comprehensive bibliometric analysis using data extracted from the Web of Science Core Collection (WoSCC), a globally recognized citation database. The search strategy was designed to capture peer-reviewed literature on construction robotics, focusing on journal articles, reviews, conference proceedings, and early-access publications. The literature in the English language includes journals indexed in SCI, SSCI, AHCI, CPCI-S, and CPCI-SSH, among others. The search query for the research used the following keywords: \"construction robot\"' OR \"building robot\"' OR \"construction robotic\"' OR \"building robotic\"' OR \"robotic\" in construction\" OR \"robotic\" for construction\" OR \"robotic\" in building\" OR \"robotic\" for building\". The search spanned publications from 2002 to 2024 to track both foundational and emerging trends. After removing duplicates, editorials, and non-English documents, 212 relevant articles were retained for analysis.