esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Patient Inclusion and Exclusion Criteria  
The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Local Ethics Committee of the County Emergency Hospital \"Pius Brinzeu\" Timisoara (nr. 235/2021). Informed consent was obtained from all subjects involved in the study.  
This prospective study included 437 consecutive patients who presented in our ultrasound unit for the evaluation of known thyroid nodules, between 1 February and 31 August 2021 (7 months). Out of these, 215 patients had an indication for thyroid FNA, according to the ultrasound appearance: low-risk nodules if larger than 2 cm, intermediate-risk nodules if larger than 1.5 cm, and high-risk nodules if larger than 1 cm; in high-risk nodules between 0.5 and 1 cm, FNA was performed only when individual, anamnestic risk factors were present [9]. Confirmed Bethesda II nodules with a visible goiter or objective compression and nodules in Bethesda categories III-VI were referred to surgery and the pathology reports were obtained. By the end of September 2021, 90 cases had the final pathology report. The pathology report, not the FNA report, was considered the golden standard for defining thyroid cancer. Only these cases were included in the final analysis. The management decision was in accordance with the ATA guidelines for thyroid nodules [4]. The concomitant presence of autoimmune thyroid disease (AITD) was not an exclusion criterion. The patients were evaluated clinically and by means of ultrasound. Conventional US of the neck was performed in all patients, followed by examination with 2 different elastography techniques: SE and 2D-SWE, performed with a maximum interval of 6 weeks between them. All US examinations were made before performing FNA.  
The following cases were excluded from the analysis: (i) nodules that did not have both elastography evaluations within 6 weeks; (ii) mixed lesions with a predominantly cystic structure; (iii) cases that did not have the pathology report at the time of inclusion; (iv) deeply located nodules.
# A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones
## 4 Neutralization Systems
### Electronic Neutralizers
#### 4.1.3 Spoofing
Finally, spoofing applied to on-board sensors also deserve some attention. In this case, the spoofing source sends false signals to sensors, which can lead to the destabilization of the malicious drone control system. For example, as indicated in [91] and in references reported there, gyroscopes and accelerometers are sensitive to ultrasound at their resonant frequency and this vulnerability can be attacked. In [92], the authors spoofed the gyroscope of a drone, causing it to land. However, these attacks require powerful speakers and are limited in range due to the degradation of the sound wave with distance. Furthermore, a reference for a possible solution to an acoustic attack is always present in [91]. Other sensors that can be spoofed are those of the optical flow type. Their vulnerability was demonstrated in [93], where a method was presented to hijack a drone by spoofing the camera (thus affecting the stabilization algorithm) by means of a laser and a projector aimedat the drone's surface. Again, in [91], there was evidence of the fact that the presence of a magnetic field in the vicinity of a drone, in this case the DJI Phantom, always requires the recalibration of the relative magnetometer before take-off.
###### Abstract  
The polygenic risk score (PRS), together with the \(\epsilon 4\) allele of the _APOE_ gene (_APOE-\(\epsilon 4\)_), has shown high potential for Alzheimer's disease (AD) risk prediction. The aim of this study was to validate the model of polygenic risk in Russian patients with dementia. A microarray-based assay was developed to identify 21 markers of polygenic risk and \(\epsilon\) alleles of the _APOE_ gene. This case-control study included 348 dementia patients and 519 cognitively normal volunteers. Cerebrospinal fluid (CSF) amyloid-\(\beta\) (A) and tau protein levels were assessed in 57 dementia patients. PRS and _APOE-\(\epsilon 4\)_ were significant genetic risk factors for dementia. Adjusted for _APOE-\(\epsilon 4\)_, individuals with PRS corresponding to the fourth quartile had an increased risk of dementia compared to the first quartile (OR 1.85; \(p\)-value 0.002). The area under the curve (AUC) was 0.59 for the PRS model only, and the inclusion of _APOE-\(\epsilon 4\)_ improved the AUC to 0.604. PRS was positively correlated with t2 au and p2 Tau181 and inversely correlated with A4\(\beta 42\)/A\(\beta 40\) ratio. Carriers of _APOE-\(\epsilon 4\)_ had higher levels of t2 au and p2 Tau181 and lower levels of A\(\beta 42\) and A\(\beta 42\)/A\(\beta 40\). The developed assay can be part of a strategy for assessing individuals for AD risk, with the purpose of assisting primary preventive interventions.  
dementia; polygenic risk; Alzheimer's disease; _APOE_; microarray; genetic risk; neurodegenerative diseases +
Footnote †: journal: Molecular Sciences
# Fundamental Matrix Computing Based on 3D Metrical Distance
###### Abstract  
To reconstruct point geometry from multiple images, computation of the fundamental matrix is always necessary. With a new optimization criterion, i.e., the re-projective 3D metric geometric distance rather than projective space under RANSAC (Random Sample And Consensus) framework, our method can reveal the quality of the fundamental matrix visually through 3D reconstruction. The geometric distance is the projection error of 3D points to the corresponding image pixel coordinates in metric space. The reasonable visual figures of the reconstructed scenes are shown but only some numerical result were compared, as is standard practice. This criterion can lead to a better 3D reconstruction result especially in 3D metric space. Our experiments validate our new error criterion and the quality of fundamental matrix under the new criterion.  
fundamental matrix; computer vision; RANSAC; metrical distance; structure from motion 1
## 2 Research Methodology
### Analysis of the Most Common Problems in the Diagnostics of the Rotor System
\begin{table}
\begin{tabular}{c c c} \hline
**Issue** & **Identification Methods** & **References** \\ \hline \multirow{8}{*}{Rotor bending} & System equivalent reduction expansion process (SEREP) & [53] \\ \cline{2-3}  & Isometric mapping (ISOMAP) & [55] \\ \cline{2-3}  & Inverse eigensensitivity (IES) method & [53] \\ \cline{2-3}  & Principal component analysis (PCA) & [28,55] \\ \cline{2-3}  & Natural frequency difference (NFD) & [29,53,55] \\ \cline{2-3}  & Characteristic equation method & [53] \\ \cline{2-3}  & Newton–Raphson method & [55] \\ \cline{2-3}  & Runge–Kutta methods & [29] \\ \cline{2-3}  & Least squares method & [28,53] \\ \hline \multirow{8}{*}{Rotor–stator rub} & Finite element method (FEM) & [49,56] \\ \cline{2-3}  & Time-frequency analysis (TFA) & [57] \\ \cline{2-3}  & Fast Fourier transform (FFT) & [9,49,57] \\ \cline{2-3}  & Hilbert transform (HT) & [9,57] \\ \cline{2-3}  & Wavelet transform (WT) & [57,58] \\ \cline{2-3}  & Gabor transform (GT) & [9,16] \\ \cline{2-3}  & Topological fractal multi-resolution analysis (TFMRA) & [9] \\ \cline{2-3}  & Empirical mode decomposition (EMD) & [57] \\ \cline{2-3}  & Method of small parameter & [56] \\ \cline{2-3}  & Newton–Raphson method & [49,56,57] \\ \cline{2-3}  & Runge–[PERSON] methods & [49] \\ \cline{2-3}  & Convolutional neural network (CNN) & [16,58] \\ \cline{2-3}  & Teager energy operator (TEO) & [58] \\ \cline{2-3}  & Newmark-\(\beta\) Method & [49,57] \\ \hline \multirow{8}{*}{Bearing faults} & Finite element method (FEM) & [59] \\ \cline{2-3}  & Time-frequency analysis (TFA) & [51,60,61] \\ \cline{2-3}  & Wavelet transform (WT) & [17,60,61] \\ \cline{2-3}  & Fast Fourier transform (FFT) & [11,17,32,38,46,51,60,62,63] \\ \cline{2-3}  & Hilbert–Huang transform (HHT) & [11,32,38] \\ \cline{2-3}  & Discrete wavelet transform (DWT) & [32,38] \\ \cline{2-3}  & Short-term Fourier transform (STFT) & [38,62] \\ \cline{2-3}  & Gabor transform (GT) & [11,16,46] \\ \cline{2-3}  & Chebyshev interval method (CIM) & [64] \\ \cline{2-3}  & Full tensor grid (FTG) & [64] \\ \cline{2-3}  & Principal component analysis (PCA) & [17,51,62] \\ \cline{2-3}  & Decision tree (DT) & [46] \\ \cline{2-3}  & Random forest (RF) & [46] \\ \cline{2-3}  & Support vector machine (SVM) & [46] \\ \cline{2-3}  & Teager energy operator (TEO) & [60] \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._The following subsections offer a comprehensive overview of some of the existing research that addresses the problem provided. The section is divided into subsections for specific issues to make it more convenient. For each paper that has been reviewed, the general concept of the research, the method used by the authors to investigate the problems provided, and the limitations of their solutions are provided.
# Time-Specified Adaptive Robust Control Framework for Managing Nonlinear System Uncertainties
## 2 Background and Related Work
### Sliding Mode Control (SMC)  
Sliding mode control (SMC) is a widely utilized robust control technique for nonlinear systems, known for its effectiveness in handling model uncertainties and external disturbances. The fundamental concept behind SMC is to create a sliding surface that compels the system to move along it, ensuring both system stability and the fulfillment of performance criteria. In this approach, a sliding surface (or sliding hyperplane) is initially defined, usually as a function of the system's states:  
\[S(x)=Cx+d=0 \tag{1}\]  
In this context, \(S(x)\) represents the sliding surface, while C and \(d\) are design parameters. The control law employs a switching control strategy to drive the system state to move along the sliding surface. A typical expression for the sliding mode control law is  
\[u(t)=-k\cdot sign(S(x)) \tag{2}\]  
where \(k\) represents a sufficiently large positive control gain, and \(sign(S(x))\) denotes the sign function of \(S(x)\).  
Sliding mode control (SMC) offers significant advantages, particularly its high robustness, which enables it to effectively handle uncertainties in system parameters, external disturbances, and input constraints. However, one of its primary drawbacks is the \"chattering\" phenomenon, which involves high-frequency oscillations near the sliding surface. Despite this limitation, sliding mode control is widely utilized in various uncertain nonlinear systems, such as robotic control, spacecraft control, and autonomous driving.
## 4 Empirical Results
### Key Results
#### 4.2.2 Answer to RQ2 and H2  
To assess robustness across varying market regimes, a rolling-window out-of-sample evaluation was conducted. The results, visualized in Figure 5, demonstrate that the proposed model maintains stable predictive performance across different temporal segments, including both high- and low-volatility periods. In addition, residual analysis (Figures 9 and 10) confirms the absence of systematic drift or overfitting to noise. These findings support H2, indicating that the proposed VMD-Cascaded LSTM with Attention model generalizes well across changing market conditions.
# Remote Sensing Image Enhancement Based on Non-Local Means Filter in NSCT Domain
## 3 Implementation of the Proposed Method
### Steps of the Proposed Method  
1. The original image is decomposed into one low-frequency sub-band and several high-frequency sub-bands by NSCT transform.  
* The low-frequency sub-band coefficients are processed with contrast stretching according to equation 7, and the noise of the first high-frequency sub-band coefficients is suppressed by the NLM filter according to equations 3-6.
* The adjusted coefficients are reconstructed with inverse NSCT transform.
* The reconstructed image is enhanced by the unsharp filter.  
The flow chart of the proposed method is shown in Figure 4.
# Diverse Humanoid Robot Pose Estimation from Images Using Only Sparse Datasets
## 2 Related Work
### Robot Arm Pose Estimation  
Research on robot arm pose estimation has recently been conducted by using neural networks without relying on markers or additional sensors [41; 42; 5; 6; 7; 43]. In one approach, the 2D pose of a robot arm is estimated from a single RGB image using a CNN, followed by camera-to-robot pose estimation using the perspective-n-point (PnP) algorithm [6]. Another study, bypassing the PnP algorithm, directly estimates the robot's 3D joint posebased on its mask, predicted using CNN. This method estimates both the robot's location and its 3D joint coordinates using a freely moving camera, overcoming the limitations of a fixed camera-to-robot setup [5]. Furthermore, [PERSON] et al. [7] proposed a methodology for more accurately estimating the pose of robot arms by applying keypoint optimization and sim-to-real transfer approaches. Similarly, [PERSON] et al. [41] introduced a method for predicting a robot pose in real time without relying on prior structural knowledge of the robot.  
There have also been studies employing sequence data rather than single images [42; 43]. A study proposes a real-time camera-to-robot pose estimation model, SGATPose, by utilizing robot structure priors from single-view successive frames in an image sequence [42]. Furthermore, another study contributes to risk mitigation in human-robot collaboration scenarios by using a Recurrent Neural Network (RNN) model to predict the robot's movements [43]. [PERSON] et al. [8] proposed an Avatar Robot System that enables visual and haptic interactions by aligning a human model onto the robot arm. These methods may be effective for estimating the motion of simple robotic arms, but there are limitations when applied to complex movements involving multiple joints and body parts, as seen in humanoid robots. To accurately estimate such complex motions, approaches that can model the intricate interactions between different parts with greater precision are required.
## 4 Performance Analysis and Simulation Outcomes  
In this section, the simulation outcomes under various scenarios are provided for the IRS-aided D2D cellular network to prove the effectiveness of the RL-based proposed solution schemes and to analyze the impact of different parameters on the system performance.
# Surface Electromyography-Based Action Recognition and Manipulator Control
## 2 Method and Mathematical Background
### Data Preprocessing  
The baseline wander was removed through a median filter with length \(L=11\). The 50 Hz power frequency interference was got rid of by a Butterworth notch, whose frequency bandwidth was 49-51 Hz, with a pass-band ripple \(r_{p}=1\) dB and a stop-band attenuation \(r_{s}=50\) dB. The acquisition circuit contained wireless communication, so it would inevitably bring a high frequency interference from surroundings. We made use of a 256 th order Chebyshev filter, to filter out the high frequency interference. The bandwidth was 10-300 Hz. All filters were implemented through software (QtCreator).  
There is a demand to increase processing speed and decrease delays. Additionally, due to the fact that the forearm of the subject is not always straight, the system must be able to distinguish the start and end of the action. In this study, the overlapping sliding window was adopted and the data were divided into multiple windows. In this way, only the sEMG signals in a small window needed to be analyzed each time to judge whether an action happened. Compared with other characteristics like the frequency-domain feature, the amplitude of the EMG signals was more related to the corresponding action. When the subject made an action, the change of amplitude of the EMG signals was more obvious and easier to be captured intuitively. As a result, Mean Absolute Value (MAV) of a window was calculated and represented as energy \(Q\). If \(Q\) was greater than a set threshold \(A\) more than N times, it was considered that an action had started. When \(Q\) was less than \(A\), it was believed that this action ended. In accordance with references and multiple experiments, we set the window size to 128 ms, the sliding step to 50 ms, and the threshold \(A\) to 1.5 times the \(Q\) of the resting state. After dividing the window, \(A\) was calculated and saved for judging the start and the end of the action.  
Figure 1: Schematic diagram of the proposed sEMG detection and action recognition system. Raw multi-channel sEMG signals were first acquired and transmitted wirelessly to the host computer. Then the signals were filtered and divided into sliding windows. In each window, feature extraction and action classification were conducted. The output label of the classifier drove the manipulator via the corresponding control command. The action consistency of the subject and the manipulator was observed in real time.
## 4 Experimental Results
effectively to the new speed commands and has better disturbance rejection capabilities. By stabilizing the torque faster, the proposed strategy ensures a more responsive system, which is advantageous for applications that require rapid adjustments. Figure 11 compares the performance of the proposed control strategy under precise machine parameters (before the red line) and with a \(-30\%\) deviation in self-inductance (after the red line). The electromagnetic torque remains stable and exhibits minimal ripple even under parameter uncertainties, demonstrating effective torque regulation. The decoupled currents idq0 are well-regulated, while slight variations appear after the red line, the control strategy effectively mitigates disturbances and maintains steady decoupling. Similarly, the phase currents \(i_{\text{abc}}\) remain sinusoidal and balanced, reflecting robust current regulation despite parameter deviations. The proposed strategy ensures stable performance under significant uncertainties, making it suitable for practical applications in which machine parameters can vary due to manufacturing tolerances or operating conditions. Its robustness enhances the reliability and performance of VFRM. Furthermore, while the ITHESO demonstrates robustness to parameter mismatches, its performance could degrade under extreme parameter variations or unmodeled nonlinearities. Fine-tuning the observer gains is necessary to maintain optimal performance across all operating conditions. Overall, these improvements make the proposed method particularly advantageous for EV applications. Torque ripple reduction minimizes mechanical vibrations and noise, leading to improved passenger comfort and quieter operation. The method's robustness to parameter mismatches and external disturbances ensures reliable performance under varying speeds and loads, which are common in EV drive cycles.
## 2 Related Works
The results of their study showed that this protocol is much more secure than traditional protocols, and the integration of additional authentication factors significantly reduced the attack success rates [6], but taking into account the price paid through the complexity of the proposed model and the primitive management model for cryptographic keys. Moreover, an extremely important topic in IoT security is the risks associated with data management and storage. [PERSON] (2020) proposes a cryptographic key generation and renewal system for IoT nodes, focusing on protecting data in a distributed network, where cryptographic keys are constantly updated to prevent their compromise. His studies have shown that the constant updating of cryptographic keys significantly reduces the risks of attacks on stored data, thus improving the overall security of IoT networks [7]. This approach is essential to ensure the integrity and confidentiality of data in large IoT networks, where the volume of data and the number of devices involved are extremely large. Also, the modeling and analysis of IoT vulnerabilities is a fundamental part of recent research in the field. [PERSON] et al. (2019) examined the requirements and recommendations for IoT and IIoT models intended for the automation of security assurance, by using penetration tests and threat analysis. The results of their studies confirmed that the use of a robust test model allows early identification of vulnerabilities, so that protection measures can be implemented more effectively [8]. In this line, [PERSON] et al. (2019) conducted an exhaustive review of IoT vulnerabilities, providing a guide for assessing their exploitation on a global scale. Their results demonstrated that the constant analysis of vulnerabilities is crucial for the protection of data and IoT networks, having a significant impact in preventing attacks [9]. The security of IoT networks significantly depends on advanced encryption and authentication technologies. [PERSON] and [PERSON] (2008) developed TinyECC, a configurable elliptic curve cryptography library that is optimized for wireless sensor networks. Their results showed that this solution significantly improved data protection in large IoT networks, being widely adopted due to its high performance and ease of implementation [10]. This solution has been successfully used in multiple IoT implementations, demonstrating its effectiveness in ensuring a high level of security, even in conditions of limited resources. Therefore, recent research reflects significant progress in IoT security, emphasizing the importance of effective cryptographic solutions and key management strategies adapted to the limited resources of IoT devices. Each of the mentioned studies contributes to the consolidation of a more robust security framework, oriented towards improving performance and data protection in IoT networks. Their results validate the proposed solutions and demonstrate their applicability in real IoT scenarios.