esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Consensus on Challenges
#### 4.1.5 Unfriendly Interface  
The use of head-mounted displays was reported to be unfriendly, and practitioners were unfamiliar with these types of interfaces. Three papers mentioned that the interface could be more user friendly or customizable according to the user's needs [19; 30; 36]. In a lab setting, students suggested that the user interface for the data-finding tasks offered by the virtual manipulation of a head-mounted device was not user friendly. Similar to a construction environment, users reported that the same interface could be more friendly, or custom-made for specific users. This interface is offered only when using a head-mounted device; AR applications supported by mobile hardware are different. The interface on mobile or pad devices is offered through the screen and manipulation through finger touch movements. Therefore, some studies reported that, with training and practice, users could become more familiar with the gesture interface offered by the head-mounted device which helped in overcoming this challenge [34; 35]. This attribute is related to the type of device in use supporting the application.
# Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks
## 1 Introduction
The core advantage of deep learning algorithms lies in the direct mapping from raw data to classification results, simplifying the separation steps of feature extraction and classification in traditional methods, and enhancing the adaptability of algorithms to different data distributions [22]. However, deep learning models often lack spatial reasoning capabilities (e.g., object orientation) [23; 24] and are computationally intensive due to their large-scale architectures, which may include hundreds of layers [25], the training and inference process is usually computationally intensive and requires high hardware resources. A persistent challenge is the significant distortion of weak EEG signals [26]. Addressing these limitations, [PERSON] introduced a capsule network framework with vector-based neurons that mimics human cognitive processes and better simulates hierarchical knowledge representation in neural networks [27]. Numerous studies demonstrate the application of capsule networks across diverse domains. [PERSON] and [PERSON] developed a capsule network framework for classifying motor imagery tasks [28]. [PERSON], [PERSON] et al. introduced a capsule network architecture to decode motion direction via spike timing analysis [23]. [PERSON] designed a 1D capsule network for epilepsy classification during seizure and pre-ictal phases [29]. However, capsule networks are rarely applied to multi-class classification tasks in small EEG datasets [30]. The need for specialized personnel (1\(\sim\)2 professionals), combined with high costs (e.g., single-use EEG caps), elevated scalp-electrode impedance, and time constraints, makes it difficult to collect large-scale epileptic EEG datasets from individual subjects [31]. Thus, small sample sizes remain a significant barrier in practice, and accurate seizure identification from limited EEG data remains an unresolved challenge. We propose an epilepsy detection method based on capsule neural networks. Architecturally, the capsule network consists of many small, interconnected processing units that encode the presence, orientation, location, and other attributes of objects through capsules. Dynamic routing is employed to propagate information and better preserve spatial hierarchical relationships, thereby enabling the autonomous extraction of spatial relationships from EEG signals and the preservation of key features to improve detection accuracy. The lightweight architecture of this framework enables multi-level feature learning from EEG signals. By utilizing dynamic routing algorithms, the capsule network layer is simplified, which can enhance the accuracy of multi-class classification even with limited EEG samples.
## 3 Van Hove Singularities  
Van Hove singularities (VHS) in the density of states are attractive properties of TBG, since strong correlation effects like superconductivity, ferromagnetism and the charge density wave could be induced when the system Fermi level is tuned to van Hove singularities energy levels. The VHS of TBG at low energy come from the saddle points formed when the Dirac cones of each graphene layer cross and merge together, as shown in Figure 2a [40]. Obviously, the VHS are always in a pair and are symmetric to the charge neutral point (CNP) in energy levels.  
Actually, van Hove singularities are the first unique and well-studied phenomenon in TBG. The experimental studies on the moire flat band, superconductivity, etc., are later inspired by the discovery of van Hove singularities. The earliest experimental work of the van Hove singularity in TBG are carried out by [PERSON]'s group, with scanning tunneling microscopy (STM) [3]. They have found a pair of sharp peaks symmetrical to the charge neutrality point in the scanning tunneling spectroscopy (STS) taken on the highly oriented pyrolytic graphite (HOPG) surface with the moire pattern, as shown in Figure 2b,c. With the help of the theory, they could assign these peaks to the saddle points of the merged two Dirac cones, e.g., VHS levels. Later, they investigated the influence of the twist angle to the van Hove singularity energy levels by investigating chemical vapor deposition (CVD) grown TBG samples with various twist angles [4]. By measuring the Landau level positions under a high magnetic field, the Fermi velocity \(v_{F}\) could be deduced. It is found that the v\({}_{F}\) of large twist angle TBG is similar to monolayer graphene, while the \(v_{F}\) of the small angle TBG is significantly lower, for example, \(v_{F}=0.87\times 10^{6}\) m/s when \(\theta=1.16^{\circ}\); \(v_{F}=1.1\times 10^{6}\) m/s for monolayer graphene [41], as shown in Figure 2d.  
On the twisted multilayer graphene epitaxial grown on 6H-SiC(0007), [PERSON]'s group systematically investigated the VHS levels by measuring the STS on samples with varying twist angles [40], as shown in Figure 2e. They have found that \(\Delta E_{VHS}=2\hbar v_{F}\Gamma_{K}sin(\theta/2)-2t_{\theta}\), where \(v_{F}\) is the Fermi velocity, \(\Gamma_{K}\) is the wave vector of the Dirac point in monolayer graphene and \(t_{\theta}\) is the modulus of the amplitude of the main Fourier components of the interlayer potential for monolayer graphene. [PERSON]'s group [5] reported similar results on TBG samples grown on Rh foil, and reveal the coupling between epitaxial graphene and substrate could also influence the energy levels of VHS. These results are verified again by [PERSON] et al. [30] on epitaxial 19.1\({}^{\circ}\) TBG on Cu foil by direct observation of the saddle point on the valance band of TBG by \(\upmu\)-ARPES, as shown in Figure 2f.
# Flexible Simulation Platform for Generating Realistic Waveforms with Voltage Notches
## 7 Discussion  
The comparison of simulations with field measurements showed strong similarities in the distributions and ranges of key features, such as \(d_{com}\), \(\Delta t_{com}\), and current harmonic components. Statistical metrics, including mean, range, and relative error, indicated good agreement between the simulated and measured data. The correlation analysis revealed that the simulation model effectively captures the interdependencies among the features, although some discrepancies were observed, particularly involving \(d_{com}\) and \(\Delta t_{com}\).  
These discrepancies may be attributed to limitations in the simulation model, such as the assumptions made about network parameters or no consideration of additional impact factors in the field measurements. For instance, variations in network impedance, unaccounted reactive power compensation devices, or differences in background harmonic distortion could influence the observed correlations and feature distributions.  
Figure 22: Examples of voltage notch (**top**) and current waveforms drawn by the LV installation (**bottom**) obtained from the Monte Carlo simulation for the case study. (**a**) Scenario 1, (**b**) Scenario 2, (**c**) Scenario 3 with non-detuned capacitor bank, (**d**) Scenario 3 with detuned capacitor bank, (**e**) Scenario 4 with non-detuned capacitor bank, and (**f**) Scenario 4 with detuned capacitor bank.  
A limitation of the validation process presented in this paper is the limited variability of the field measurements, as only one rectifier in an industrial installation operating at almost steady conditions could be considered. This limitation impacts the generalizability of the results, as the validation is based on a narrow set of conditions. To enhance the robustness and applicability of the simulation platform, future work should involve the extension of the dataset with additional field measurements from different installations including varying operating conditions. This would allow for a more comprehensive validation and increased confidence in the simulation model's performance across different scenarios.  
The slight phase shift observed in the fundamental current phase angle between simulations and measurements highlights the importance of accurately modeling all relevant factors, including background harmonic distortion and reactive power compensation devices. Addressing these discrepancies in future enhancements of the simulation platform could further improve its predictive accuracy.
# An Improved Hybrid Control Scheme of a Switched Reluctance Motor for Torque Ripple Reduction
## 4 Speed Controller Based on Modified LADRC
Thus, a linear extended-state observer with variable gain is designed to improve the anti-interference ability and reduce the overshoot simultaneously, which solves the contradiction resulting from fixed gain parameters. The bandwidth of the linear extended-state observer \(\omega_{0}\) can be described as  
\[\omega_{0}=\frac{c_{1}}{1+e^{-c_{2}t}} \tag{22}\]  
where \(t\) is the run time, \(\omega_{0}\) is a time-varying function, \(c_{1}\) and \(c_{2}\) are tunable parameters, \(c_{1}>0\), and \(c_{2}>0\). According to (21), the adjustable gain parameters, \(\beta_{1}\), \(\beta_{2}\), \(\beta_{3}\), and \(k_{p}\), are associated with \(\omega_{0}\). Obviously, \(\omega_{0}=c_{1}/2\) when \(t=0\) s and \(\omega_{0}\) approaches \(c_{1}\) when \(t\) increases gradually. The lower and upper values of \(\omega_{0}\) are \(c_{1}/2\) and \(c_{1}\), respectively. The value of \(\omega_{0}\) is equal to \(c_{1}/2\) when the run time \(t=0\) s, which is smaller than \(c_{1}\) and beneficial to overshoot suppression and stability. Then, the value of \(\omega_{0}\) increases gradually with increased time. After the system reaches a steady state, \(\omega_{0}\) is relatively large, thereby enhancing the performance in fields of anti-interference ability and tracking accuracy. The value almost reaches the maximum value of \(c_{1}\) when the system runs stably for a long time, which guarantees controllability. Compared with a fixed value of \(\omega_{0}\) in conventional LADRC, the value of \(\omega_{0}\) in the proposed design varies with run time during the operation of the motor. The shape of change function is determined by the values of \(c_{1}\) and \(c_{2}\), which depend on the given condition of the used motor, such as given reference speed and load torque. The dichotomy method is applied in this study for parameter debugging, such as \(c_{1}\) and \(c_{2}\), finally selecting the parameters that improve dynamic performance both in terms of speed and torque. In general, values are determined by trial by experience and experiment. \(\omega_{0}\) varies with run time during the operating period. In general, the modified linear extended-state observer with proportional gain and time-varying function achieves superiority in terms of fast response, anti-disturbance ability, overshoot suppression, and tracking performance.
## 2 Materials and Methods
### Instrumentation  
Height (m) was measured using a seca(r) 222 stadiometer (seca--Medical Scales and Measuring Systems(r), Birmingham, UK), with a 1 mm scale. Body mass (kg) was assessed using a seca(r) 760 scale (seca--Medical Scales and Measuring Systems(r), Birmingham, UK), which had a precision of 0.1 kg. The KINETIKOS CE-marked (Class I medical device) cloud-based platform (KINETIKOS, Coimbra, Portugal) was used to collect and reconstruct, in real time, subjects' motion using a 3D kinematics model and 5 inertial measurement units (IMUs) (MVN BIOMECH Awinda, Xsens Technologies, Enschede, The Netherlands), according to the ISB recommendations (50). With an acquisition frequency of 100 Hz, the KINETIKOS platform was also used to define the beginning, the respective phases, and the end of the shoulder movement or task. Four degrees of freedom (DoF) described the scapula's kinematics utilizing the joint reference frame x-y-z but with joint origin at the centroid of the scapula's Angulus Acromialis, Trigionum Spinae, and Angulus Inferior (51). Therefore, scapulothoracic motion was described, relative to the thorax, as abduction (positive) followed by elevation (positive) on an ellipsoidal thoracic surface, winging--the raising of the scapula's medial border and inferior angle from the thorax--(positive), and upward rotation (positive). The model of the scapulothoracic joint, validated through comparison with bone-pin markers, exhibited an accuracy of 1.85 mm for position (measurement error ranging from 0.40 to 2.4 mm) (51). Shoulder motions, particularly elevation (positive), were described using three DoFs, considering the humerus relative to the thorax. The model also incorporated three DoFs to describe the trunk movements (to assess possible compensatory movements), considering the thorax sensor's rotation relative to itself, as lateral flexion (positive towards the side of the assessed upper limb), axial rotation (positive towards the side of the assessed upper limb), and forward flexion (negative). Additionally, the KINETIKOS CE-marked (Class I medical device) cloud-based platform (KINETIKOS, Coimbra, Portugal) was used for data processing and analysis. The wireless Trigion(tm) acquisition system (Delsys Inc., Natick, MA, USA) was used to record the surface electromyographic (EMG) signal from the three trapezius portions, SA portions, and LS. For this purpose, 6 pre-amplified sensors (Trigno Avanti Sensor model, Delsys Inc., Natick, MA, USA) were employed, with a 4-bar formation of Ag bar surface electrodes and an inter-electrode distance of 10 mm. These sensors were pre-amplified
## 4 Results of Numerical and Experimental Approaches
Figure 10: Comparison of time–temperature characteristics for regions containing two selected reinforcement bars—rebar 4 (R4) and rebar 5 (R5)—based on experimental and numerical results for the configuration with a 30\({}^{\circ}\) inclination: (**a**) identification of the regions encompassing both rebars on a selected experimental thermogram, (**b**) corresponding indication of these regions on a representative numerical thermogram, (**c**) comparison of the time–temperature profiles for rebar 4 (blue: numerical result; red: experimental result), and (**d**) analogous comparison for rebar 5. As evidenced by the plots, the numerical and experimental results show strong agreement, particularly in terms of the overall trend of temperature increase over time. The shape and slope of the curves remain consistent across both approaches. Moreover, the observed deviations between the two datasets do not exceed 0.3 \({}^{\circ}\)C in any of the analyzed cases, which confirms the reliability of the numerical model for capturing the thermal behavior in the vicinity of the reinforcement bars under the given conditions.
# Fast Pre-Diagnosis of Neoplastic Changes in Cytology Images Using Machine Learning
## 3 Results  
### System Setup and Implementation  
The neural network was implemented in Python, using Keras library with Tensorflow backend. In all experiments, the pre-trained YOLOv3 network was used [15] as a basis. For more information about YOLOv3 architecture please see Appendix A. Training and testing flow is depicted in Figure 8.  
A series of experiments were conducted to assess the model and the approach proposed in this work. Using multiple metrics (please refer to Appendix B for details) and various tests enabled a comprehensive presentation of the model and the introduced methodology.  
In the preliminary experiments and the model calibration, a range of hyper-parameters was chosen, making further exhaustive tests possible. They are specific to the dataset and the proposed approach. The most important ones are:  
* anchor boxes values: {(18,22); (18,28); (22,31); (23,25); (24,38); (28,31); (30,43); (36,36); (40,48)]. They are consecutive pairs (the first one is an 18 \(\times\) 22 frame), fitted using the K-Means algorithm. These are the predefined frames that best match those of the training set, and the network learns how to move and zoom them to match the labels;
* Objectness threshold--the threshold used to distinguish between object and non-object: 0.5;
* IoU The threshold used to decide when detection is positive or negative: 0.5.  
These parameters are used across the experiments presented in this section unless other values are explicitly stated. The code used for the experiments is available at [[https://git.plgrid.pl/projects/CYFROVET/repos/vet-detection](https://git.plgrid.pl/projects/CYFROVET/repos/vet-detection)]([https://git.plgrid.pl/projects/CYFROVET/repos/vet-detection](https://git.plgrid.pl/projects/CYFROVET/repos/vet-detection)), accessed on 10 July 2021.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Dataset Version**} \\ \cline{2-6}  & **Initial** & **Divided** & **Big** & **Balanced** & **Segmentation** \\ \hline \# of photos & 1219 & 3560 & 7120 & 4337 & 87 \\ \# of objects & 28,149 & 25,199 & 50,398 & 28,337 & 2408 \\ Avg. objects per photo & 23.1 & 7.1 & 7.1 & 6.5 & 27.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of created datasets.  
Figure 7: Conversion of the image from the segmentation dataset for the detection experiments. (**a**) Segmentation polygons. (**b**) Detection frames.  
All of the 'divided', 'big', and 'balanced' datasets were split into 70-20-10% training-validation-testing subsets. The presented proportion was selected to ensure that the protocol's validation step is stable. Usually, the validation set is smaller (e.g., 5%), but we wanted to guarantee the stability of the training process and appropriate coverage of all the classes, which was especially important for classes with a low sparsity of labeled cells.  
For the 'divided' dataset we present the performance for all subsets (see Table 3). To compare different datasets we only use testing subset.
# Fusing Appearance and Prior Cues for Road Detection
## 3 Experimental Results and Discussion
### Experimental Results  
#### 3.2.1 Evaluation of Variant Combinations of Color Channels  
As described in Section 2.1.3, shadow-free and highlight-free grayscale images are obtained by illuminant invariant transformation. To recover detail information and provide higher discriminative power, HSV color space is combined with the log-chromaticity space.  
In order to evaluate the performance of variant combinations of color channels, we use receiver operating characteristic (ROC) curves on the comparison between the results of road detection and ground-truth provided in the KITTI road benchmark training set. ROC curves show the trade-off between the true positive rate (TPR) and the false positive rate (FPR) at various threshold \(\lambda\) settings, which are defined as follows:  
\[TPR=\frac{TP}{TP+FN}, \tag{14}\]  
\[FPR=\frac{FP}{FP+TN}, \tag{15}\]  
where \(TP\) means true positive, \(FN\) means false negative, \(FP\) means false positive, and \(TN\) means true negative. In addition, we use the area under the curve (AUC) for performance comparisons. The higher the value of the AUC, the higher the accuracy. Results are shown in Figure 5.  
As shown, higher performance is achieved when H, S, and V channels are combined with the log-chromaticity space. However, the worst performance appears when all of the log-chromaticity and H, S, and V channels are used simultaneously since the robustness to shadows and highlights of the log-chromaticity space is badly weakened by the entire HSV color space. From these results, we can conclude that the highest performance is provided by combining the log-chromaticity space and the S channel in HSV color space.
## 3. Designed Scheme
### Authentication and Reconstruction  
This section describes the authentication and reconstruction process of a given tampered image _Ihw_ of size \(A\times B\) in RGB color space. To accomplish this reconstruction, four steps are used: extraction of the watermarks from the image, authentication of the content, post-processing to indicate the blocks affected by the tampering coincidence problem via an inpainting process to fill these regions and reconstruction of the tampered image.
## 3 Machine Learning Approaches for Slope Deformation Prediction
### Data Preprocessing
#### 3.2.2 Data Standardization  
Before establishing the time-series prediction model, it is necessary to standardize the series data to eliminate the influence of dimensions. In this paper, the maximum and minimum normalization method was used to normalize the displacement data to [0, 1]. The calculation method is as shown in Equation (1).  
\[x^{{}^{\prime}}=\frac{x-x_{min}}{x_{max}-x_{min}} \tag{1}\]  
where \(x^{{}^{\prime}}\) represents the calculated value, \(x_{max}\) is maximum value in the sequence data, and \(x_{min}\) is minimum in the sequence data.