esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries  
We review the operation of bilinear pairings [27; 28] and the technique of the linear secret-sharing scheme (LSSS) [29] in this section.  
_Bilinear Pairing_  
Let the symbols of \(\mathbf{G}_{1}\) and \(\mathbf{G}_{2}\) be two multiplicative cyclic groups in which the order is a prime number \(p\) and \(g\) is the generator of the group \(\mathbf{G}_{1}\). A bilinear map is expressed as \(e\): \(\mathbf{G}_{1}\times\mathbf{G}_{1}{\rightarrow}\mathbf{G}_{2}\) with the following properties:  
1. _Bilinearity:_  
For all elements \(g\), \(h\in\mathbf{G}_{1}\) and \(a\), \(b\in_{R}Z_{p}\), we have \(e(g^{a},h^{b})=e(g,h)^{ab}\);
2. _Non-degeneracy:_Let a generator in the group of \(G_{1}\) be \(p\). Then the value \(e(p,p)\) would also be a generator in the group \(G_{2}\);  
**Computability:**  
There is an efficient algorithm to compute \(\mathrm{e}(g,h)\) for all elements \(g\), \(h\in G_{1}\).  
Linear Secret Sharing Scheme (LSSS)[29]Let \(M_{l\times n}\) be a matrix with the row number of \(l\) and the column number of \(n\), \(M_{i}\) the \(i\)-th row of the matrix and a mapping function \(\rho\): \(\{1,2,\ldots,l\}\)\(\rightarrow\)**P** which converts a row to a label of party. We say that a secret-sharing scheme \(\Pi\) for the access structure \(\mathbb{A}\) over a set of parties **P** could be denoted as a linear secret-sharing method (LSSS) from \(Z_{p}\). It is also represented as (\(M_{l\times n}\), \(\rho\)), which consists of the following two effective algorithms:  
**Share(\(\langle M_{l\times n}\), \(\rho\rangle\), s):** The share algorithm takes a secret-sharing value \(s\in_{R}Z_{p}\) as an input, randomly chooses \(y_{2}\),..., \(y_{n}\in_{R}Z_{p}\) and defines \(\mathbf{v}=(s,y_{2},\ldots,y_{n})\). At last, it outputs \(M_{l\times n}\cdot\mathbf{v}\) as the vectors of \(l\) shares. That is, the shared value obtained by the participant \(\rho(i)\) is \(\lambda_{i}=\langle M_{l\times n}\cdot\mathbf{v}\rangle\);
**Recon(\(M_{l\times n}\), \(\rho\)), D):** The reconstruction algorithm takes an access set \(\mathbf{D}\in\mathbb{A}\) as the input. Let \(\textbf{I}=\{i\ |\ \rho(i)\in\textbf{D}\}\). It will return a set of constants \(\{\mu_{i}\}_{i\in\textbf{1}}\) fulfilling that \(\Sigma_{i\in\textbf{1}}\)\(\mu_{i}\cdot\lambda_{i}=s\).
# A Novel Shadow Removal Method Based upon Color Transfer and Color Tuning in UAV Imaging
## 1 Introduction
The proposed work aims to correct the chromatic discrepancies found in concrete and asphalt areas in UAV-captured images under light and shadow conditions. This method consists of a proposed local color transfer algorithm that executes the color correction separating the colors' statistics according to chromatic features into an image, uses a dilation process to the shadow mask, and a final color tuning that enhances the local color transfer results. The present proposal offers a shadow removal tool that improves the results presented in local color transfer algorithms when UAV-captured images are used. Moreover, this work considers both cast shadows and self-shadows. The proposed final color tuning step reduces the discontinuity found in similar regions that are found under shadows. In addition, this method presents an algorithm with a relatively low computational load that, at the same time, is suitable for implementing in a parallel computational model, owing to the independence of its functions. This new methodology was tested over aerial urban RGB (Red, Green, and Blue) images captured using a standard drone device. The study cases considered were captured at urban scenes containing mainly asphalt and concrete regions; such urban scenes are covered by shadow at different ratios in order to test the proposed color correction method. Likewise, the proposed work was also compared against algorithms in the state-of-the-art by visual qualitative analysis and quantitatively using the shadow standard deviation index (SSDI). The experiments demonstrated an improvement in color correction results at asphalt and concrete regions, and at the same time, accurately preserved texture information. In the following sections, we present the proposed method development describing the algorithm and method details; the results section, where the results of the experiments are exposed; the discussion section follows. Finally, the conclusions are stated.
## 2 Proposed Topology and Principle of Operation  
The proposed topology can be subdivided into three subcircuits as shown in Figure 3: a buck-boost converter, a common ground flying capacitor, and the transformerless inverter, which contains two main switches in addition to two freewheeling switches with series diodes to generate the required unipolar SPWM.
## 4 Discussion
### Application to the Clinics  
Our results give practical suggestions for clinical practice and open the way to novel research topics and scientific questions. The use of instrumented multi-domain measurements (such as EMG, force, NIRS, kinematics) might help in understanding the complex process underlying motor recovery in rehabilitation [28]. Multi-domain approaches, in particular, may help in providing a wide spectrum of assessments that might be complementary or provide information of different nature [41; 42]. Future works will contextualize the presented data with a broader view also including the acquired TD NIRS data for a complete multi-domain approach aiming at characterizing patients' motor functionality.  
Preliminary studies showed that results from a multi-domain approach may contribute to clinical evaluation and decision-making process. For example, using measurements of force, EMG and nerve stimulation, individuals with intellectual disability showed different neuromuscular profiles and recovery kinetics that have to be considered during prescription of training programs [43]. Moreover, both muscle activation patterns and kinematics are altered during fatigue [44] and they should be monitored during fatiguing exercises and rehabilitation in order to prevent injuries related to fatigue [45; 46].
###### Abstract  
The development of automatic emotion detection systems has recently gained significant attention due to the growing possibility of their implementation in several applications, including affective computing and various fields within biomedical engineering. Use of the electroencephalograph (EEG) signal is preferred over facial expression, as people cannot control the EEG signal generated by their brain; the EEG ensures a stronger reliability in the psychological signal. However, because of its uniqueness between individuals and its vulnerability to noise, use of EEG signals can be rather complicated. In this paper, we propose a methodology to conduct EEG-based emotion recognition by using a filtered bispectrum as the feature extraction subsystem and an artificial neural network (ANN) as the classifier. The bispectrum is theoretically superior to the power spectrum because it can identify phase coupling between the nonlinear process components of the EEG signal. In the feature extraction process, to extract the information contained in the bispectrum matrices, a 3D pyramid filter is used for sampling and quantifying the bispectrum value. Experiment results show that the mean percentage of the bispectrum value from \(5\times 5\) non-overlapped 3D pyramid filters produces the highest recognition rate. We found that reducing the number of EEG channels down to only eight in the frontal area of the brain does not significantly affect the recognition rate, and the number of data samples used in the training process is then increased to improve the recognition rate of the system. We have also utilized a probabilistic neural network (PNN) as another classifier and compared its recognition rate with that of the back-propagation neural network (BPNN), and the results show that the PNN produces a comparable recognition rate and lower computational costs. Our research shows that the extracted bispectrum values of an EEG signal using 3D filtering as a feature extraction method is suitable for use in an EEG-based emotion recognition system.  
b 2017 10 63; doi:10.3390/a10020063
# Statistical Process Control with Intelligence Based on the Deep Learning Model
## 4 Experiment and Discussion
### Simulation Parameters of HPs  
In order to make the simulation data closer to the complex production data, the Monte Carlo simulation parameters of the seven HPs were randomly selected within a certain range, using the uniform distribution. The range of parameters is shown in Table 1. For example, parameter \(a\) of all BIM pattern samples is uniform distribution in the range \([0.4,\ 0.6]\).  
It is worth noting that the simulation results are the quality data of each HP, not the histogram. After that, interval statistics is needed to get histogram. The purpose of this simulation method is to be more in line with the actual situation. The quality data length of each sample is 500, and the number of groups of histogram is 25. The data set consists of 14,000 samples (2000 for each HP), which are randomly divided into two parts, of which 80% samples were used to train Bi-LSTM, and the rest was used for testing.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Patterns** & **Parameters** & **Parameter Value/Range** \\ \hline NOR & Mean \(\mu\), standard deviation \(\sigma\) & \(\mu=10,\sigma=0.01\) \\ BIM & Proportion \(a\), offset distance \(b_{1}\) and \(b_{2}\) & \(a\in[0.4,\ 0.6],\ b_{1}\in[\sigma,\ 2\sigma],\ b_{2}\in[\sigma,\ 2\sigma]\) \\ LI & Proportion \(a\), offset distance \(b\) & \(a\in[0.8,\ 0.9],\ b\in[1.5\sigma,\ 2.5\sigma]\) \\ RI & Proportion \(a\), offset distance \(b\) & \(a\in[0.8,\ 0.9],\ b\in[1.5\sigma,\ 2.5\sigma]\) \\ LS & The number of normal distributions \(m\) & \(m\in\{3,\ 4,\ 5\}\) \\ RS & The number of normal distributions \(m\) & \(m\in[3,\ 4,\ 5]\) \\ FT & Proportion \(a\) & \(a\in[0.6,\ 0.8]\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The simulation parameters of seven typical HPs.  
Figure 5: The structure of the multilayer Bi-LSTM.
## 3 Results
### Event-Related Potentials of EEG  
Figure 3 represents the grand average ERP of four regions. At temporal and posterior regions, we could identify the waves typically found in emotion recognition studies. While watching neutral faces, P1, N170, P2, and EPN were registered in their usual time windows. After the onset of the gradual appearance of a facial emotional expression, P1, N170, P2, EPN and LPP could be observed on the graph.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**50\% with Low Accuracy**} & \multicolumn{2}{c}{**50\% with High Accuracy**} & \\  & **Median** & **IQR** & **Median** & **IQR** & \(p\) \\ \hline total & 61.8 & 58.7–65.7 & 71.7 & 70.0–74.4 & 0.001 \\ happiness & 100 & 90.9–100 & 100 & 95.4–100 & 0.017 \\ surprise & 83.3 & 72.2–91.7 & 88.2 & 76.2–94.7 & 0.084 \\ disgust & 73.9 & 66.7–83.3 & 85.0 & 77.3–90.9 & 0.001 \\ anger & 62.5 & 45.5–70.8 & 79.2 & 71.4–85.7 & 0.001 \\ sadness & 38.9 & 29.4–50.0 & 56.5 & 47.1–66.7 & 0.001 \\ fear & 17.4 & 5.9–29.2 & 27.8 & 18.2–34.8 & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics of two subsamples with low and high accuracy of facial emotion recognition.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**50\% with Slow RT**} & \multicolumn{2}{c}{**50\% with Fast RT**} & \\  & **Median** & **IQR** & **Median** & **IQR** & \(p\) \\ \hline total & 1661 & 1541–1813 & 1164 & 1066–1267 & 0.001 \\ happiness & 1239 & 1107–1423 & 950 & 861–1023 & 0.001 \\ surprise & 1608 & 1466–1939 & 1179 & 1056–1325 & 0.001 \\ disgust & 1627 & 1359–1971 & 1138 & 1018–1331 & 0.001 \\ anger & 1754 & 1578–2193 & 1259 & 1116–1392 & 0.001 \\ sadness & 1950 & 1730–2238 & 1391 & 1165–1546 & 0.001 \\ fear & 1906 & 1565–2221 & 1334 & 1186–1537 & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics of two subsamples with slow and fast reaction times of facial emotion recognition.  
Figure 2: The overall flow of the study.  
Topographic maps (Figure 4) show the reverse activity between anterior and posterior regions. The topoplot of ERP while watching neutral faces reflects the distribution of N170 at 170 ms, posterior P2 at 200 ms, and EPN at 500 ms. The topoplot of ERP in response to the gradual appearance of an emotional expression depicts another posterior P2 at 300 ms, EPN at 800 ms, and LPP at 1300 ms.
## 5 Key 6G Technologies
### Other Perspectives
#### 5.3.10 Cell-Free Communications  
Additionally, 6G moves away from conventional orthogonal and cellular communications, adopting non-orthogonal and cell-free approaches. Users seamlessly transition between networks without manual configurations, improving QoS and overcoming handover-related challenges [58].
## 3 Case Study and Simulation
### Relative Motion Compensation  
In this Matlab simulation, since the slave satellites are independent of each other, we choose a master satellite and a slave satellite to perform the simulation. The orbit data from the GRACE mission and Beidou G2 & Beidou A2 were employed via Satellite Tool Kit (STK) because they have significantly different inter-satellite baselines and relative velocities. For both scenarios, we assume that the measurement noise is zero, and the delays including the ionospheric delay, device delay and other delays are effectively eliminated by corresponding methods represented in Table 1. The reason for this is that we want to focus on the theoretical performance of the considered relative motion compensation scheme, rather than on the degrading effects of the measurement noise and the residuals. The positioning error of the receiver is assumed to contribute 1 picosecond of timing error. The maximum decoupling error caused by relative motion  
The results show that the errors due to relative motion have been effectively eliminated. Along with the space delays and device delay correction methods mentioned above, we can infer that the system is widely applicable for the proposed operation modes and that this novel system can maintain a considerable accuracy of clock error extraction even for high-dynamic scenarios.
# Effects of Propanol on the Performance and Emissions of a Dual-Fuel Industrial Diesel Engine
## 1 Introduction
It was found that a mixture of n-butanol, diesel fuel, and pyrolysis oil can be used in an industrial compression-ignition engine without the need to modify it, improving its performance and emissions. In paper [26], the authors presented the results of the impact of propanol as an additive to diesel fuel on an agricultural engine's performance. Propanol reduced the smoke emissions of rapeseed oil but increased NOx, total hydrocarbons (THC), and CO emissions significantly. A drop in peak pressure and a slight increase in ignition delay were observed with increasing the propanol content in the diesel fuel. A propanol fraction in a blend causes improvement in the engine's performance due to the higher percentage of premixed combustion as a result of the low cetane number of propanol. Most studies on the co-combustion of propanol with other fuels in a compression-ignition engine refer to fuel mixtures. The technology of the dual-fuel engine, the concept for which is very similar to the RCCI (Reactivity Controlled Compression Ignition) engine, which is considered to be a technology significantly contributing to the reduction of exhaust emissions. This paper presents the results of the evaluation of the combustion process in an industrial compression-ignition test engine with a dual-fuel system where propanol and diesel were used.
# Hierarchical Visual Place Recognition Based on Semantic-Aggregation
## 3 Experiments and Results
### Comparison with the State-of-Art Methods  
We conducted experiments to evaluate the performance of place recognition by comparing PR curves of the following single-image-based baseline methods:  
**FabMap**[4]: A classical method for appearance-based VPR based on Bag-of-Words model;  
**VLAD**[13]: A large-scale image-based place recognition model. It can be used for place recognition and realize good performance on many datasets;  
**NetVLAD**[20]: A viewpoint-robust CNN model for VPR, which can achieve great performance on most datasets;  
**WSABI**[31]: A novel image-based place recognition model across seasons from semantic edge description on bucolic environments such as scenes with low texture and little semantic content.  
The results are shown in Figure 12. Figure 12a shows the results of experiments conducted on the North Campus Dataset, which involves severe viewpoint variations and environmental condition variations. Figure 12b shows the results of experiments conducted on the Norland Dataset, which involves severe appearance variations. The method that we proposed (red line) obtains the best performance. We think this is because our method utilizes both the mid-level convolutional features and the higher-level semantic features in the coarse matching, thus our method is robust to the viewpoint changes and environmental conditions. Moreover, the fine matching further improves the accuracy.  
The results indicate that the method that we proposed is robust to viewpoint-variant and appearance-variant conditions.
# An IVTIFN-TOPSIS Based Computational Approach for Pipe Materials Selection
## 4 Results and Discussion
### Discussion  
Both of the results from the IVTIFN\(-\)TOPSIS (E) and the IVTIFN\(-\)TOPSIS (M) model indicate that polymers are superior to the metal materials, which are consistent with that of [PERSON] et al. [54] indicating that plastic materials are suitable for sewage transport due to their excellent performances on anticorrosion. [PERSON] et al. [12] combined AHP with gray relational analysis to select similar plastic pipes for heating systems, through which PVC showed great advantages in cost saving, and PE was better in its performance on anticlogging. [PERSON] further identified that HDPE was commonly used in landfill leachate transport due to its receptivity to highly concentrated organic matter [55]. Such a result may validate the rationality of our ranking results. [PERSON] et al. [6] further identified that there was no significant difference in the application of TOPSIS and VIKOR to pipe materials selection, but the former is much simpler and time-saving in computation. This may reflect the feasibility of our proposed approach.  
From the subjective judgment of the interviewed engineers, they attached great importance to the price, corrosion resistance, and scaling resistance regarding the selection of pipe materials. Such focus indicates that cost and functional properties are still the key drivers in materials selection [56]. However, the engineers pay little attention to the environmental attributes of materials, which reflects that environmental management has not taken as a significant criterion in engineering design [49]. With raw materials being increasingly extracted, engineering design has to consider a transition towards sustainability, not only to follow the economic bottom line, but also to improve the environmental performance [57].
# Augmented Reality-Based Interface for Bimanual Robot Teleoperation
## 1 Introduction
### Previous Research
#### 1.2.3 Augmented Reality-Based Interfaces  
Human-machine interfaces are devices that allow the interaction between a human and a machine [55; 56]. If the interface is placed inside the brain or body of the human, it is known as an invasive or implanted interface [57]. On the contrary, if the interface is external to the human body, it is known as a non-invasive or wearable interface [58; 59; 60]. This work is focused on non-invasive interfaces and on how to develop this kind of interface for complex robotic applications.  
Technological advances in the creation of holograms have nowadays made it possible to have devices and software tools that allow augmented reality (AR) applications in industrial sectors [61; 62; 63; 64]. In short, augmented reality projects holograms into physical space, allowing for a more intuitive and natural interaction between human and machine [65].  
Some previous works used AR interfaces to improve robot teleoperation for industrial tasks. For example, the authors in [66] proposed a new AR interface to control a robot manipulator in order to facilitate the interaction between the user and the robot. The authors in [66] proposed a mixed reality system in order to move the end-effector of the robot system. The authors in [67] proposed a mixed reality system to allow the user to visualize the intended teleoperation command prior to the real robot motion. A similar approach was developed in [68], where a mixed reality head-mounted display enabled the user to create and edit robot motions using waypoints. The authors in [69] proposed a multimodal AR interface coined as _Sixth Sense_ that allowed the user to interact with information that was projected onto physical objects through hand gestures, arm movements, and, in some cases, blinking. The authors in [70] proposed a method for using hand gestures and speech inputs for AR multimodal interaction with industrial manipulators.  
Note that most of the AR approaches mentioned above developed solutions for robot-object manipulation tasks. Thus, to the best of the authors' knowledge, this is the first work that proposes a new AR interface for industrial, complex tasks, such as surface treatment tasks, involving a bimanual robot system.  
In addition, the interaction with the robotic system needs to be natural and intuitive, not only from the point of view of the visual feedback produced by the AR but also from the point of view of the means of sending the robot commands. All AR headsets have interaction elements based on hand tracking. As demonstrated in [5; 6; 7; 8], such prolonged interaction over time can be annoying and not ergonomic enough. This is why, similarly to [8], this work proposes the use of gamepads, which are devices ergonomically designed to be used for long periods of time.  
To the best of the authors' knowledge, this is the first work proposing an AR interface together with a gamepad for bimanual robot teleoperation.
## Nomenclature  
* **DCNN**: Deep convolutional neural network
* **2D**: Two-dimensional
* **WT**: Wavelet transform
* **DWT**: Discrete wavelet transform
* **CWT**: Continuous wavelet transform
* **WPT**: Wavelet Packet Transform
* **HT**: Hilbert Transform
* **Ada-DCNN**: Single DCNN with AdaBoost tree-based ensemble classifier
* **Hybrid Ada-DCNN**: Multi-DCNNs with AdaBoost tree-based ensemble classifier
* **FC**: Fully connected layer
* **d**: Bearing inside diameter
* **D**: Bearing outside diameter
* **f\({}_{0}\)**: Running frequency
* **D\({}_{\text{B}}\)**: Ball diameter of bearing
* **B**: Bearing thickness
* **D\({}_{\text{P}}\)**: Bearing pitch diameter
* **IR**: Bearing inner ring
* **OR**: Bearing outer ring
* **RE**: Rolling element
* **NC**: Normal condition
* **DE**: Drive end
* **FE**: Fan end
* **RPM**: Revolutions per minute
* **FD**: Frequency domain
* **TFD**: Time-frequency domain
* **ThD**: Three domains
* **FD-Net**: DCNN based on FD samples
* **TFD-Net**: DCNN based on TFD samples
* **S\({}_{i}\)**: Accuracy of the \(i^{\text{th}}\) level
* **Maximum**: Maximal accuracy of DCNNs
* **Average**: Average accuracy of DCNNs