esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Gesture Recognition System with Gesture Spotting Based on Self-Organizing Maps
## 4 Simulation and Experiment
### Simulation  
The system was configured as follows.  
**Frame size: \(P\times Q=128\times 128\).**  
**Feature vector dimension: \(D=32\).**  
**Sequence vector dimension: \(F=20\).**  
**The numbers of neurons in the SOMs: \(M_{1}\times M_{1}=M_{2}\times M_{2}=16\times 16=256\).**  
**Moving average: \(L=4\) in Equation (17).**  
**The number of gesture classes: \(H=9\).**Data set for the simulation was vector sequence taken from video frames of recorded gesture video. Nine types of gesture shown in Figure 4 were used for the test. We defined the gesture by using the Cambridge Hand Gesture Data set [29] for reference. As Figure 4 shows, the data set consisted of nine classes. Each class gesture was defined with 10 frames, therefore the dimension of the sequence vector \(\overrightarrow{X}\) was 20. Class labels 1 to 9 are assigned to every types of gesture. Note that the labels are used to distinguish class of the gesture types, and the number does not represent numerical character. Gesture motions are combinations of three basic poses (Flat, Spread, V-shape) and three movements (Leftward, Rightward, Contract). Thus, the gesture classes are made of three groups, i.e., 1-2-3, 4-5-6, and 7-8-9. Note that the last posture of gesture 1 is also the first posture of gesture 2, and the last posture of class 2 is the first posture of gesture class 3. The other gesture groups were designed in the same way so that the gesture classes in the same group could be performed seamlessly. The number of training vectors per class was 50, and 100 test vectors were used.  
For comparison purpose, recognition performance of the JRNN [30] was examined using the same feature vectors \(\overrightarrow{G}\). Table 1 summarizes the recognition accuracies of the two methods.
# An IoT-Based System for Efficient Detection of Cotton Pest
## 1 Introduction
### Related Work
#### 1.1.2 IoT in Agriculture  
IoT devices have been investigated for their use in agriculture in developed countries. In recent times, IoT solutions have made their mark in many areas of human life, the environment, and industries. These IoT devices are also capable of exchanging data with their gateway. Smart agriculture is a major IoT application area along with smart health care, smart cities, smart industries, autonomous vehicles, smart homes, and others.  
Authors in [19] designed an IoT-based agriculture framework that explains the relationship between pests, diseases, and weather parameters. In a detailed IoT technology discussion related to agriculture [20], the researchers discussed several aspects of IoT technologies and their possible use in smart agriculture. The authors in [21] discussed the selected aspects of IoT in agriculture, sensors, and network technologies, while in [22], the authors discussed integrated pest management technologies and their standard communication protocols. A unique concept and use of IoT were discussed in [23; 24] based on various surveys, where researchers discussed IoT as an ontological tool in integrated pest management. In another study [25], the authors presented an IoT-based smart farming framework with five major components including data acquisition, platforms, processing with visualization, and complete system management.  
The rapid technological development in recent years in IoT technologies is going to play a vital role in many agriculture-related applications [26; 27]. Most authors, as cited previously, argued that most IoT architectures consist of four layers, such as (a) the perception/sensors or device layer, (b) the transport/network layer that includes protocols, (c) the processing or services layer that offers processing and analysis of data, and (d) the application layer that includes monitoring and control applications. These layers include all the main components of any IoT solution. In another study [28], authors proposed an IoT-Enabled IEEE 802.15.4 WSN Monitoring Infrastructure-Driven Fuzzy-Logic-Based Crop Pest Prediction. In this proposed architecture, pests/diseases are identified with the help of various weather factors. In [29], authors suggested an IoT-based cotton pest prediction and response system.
## 3 Key Findings
### What Security Enhancement Could the Quantum Mechanism Offer the IIoT Ecosystem?
#### 3.3.3 Quantum Deployment in IIoT  
Despite the infant stage of QC deployment, the IIoT sector has inclusively gained tremendously from the capability features of quantum security. Its merits have been felt in most strategic units of the IIoT ecosystem. In this section, the core contributions of quantum security deployment, as well as its limitations, are discussed. As discussed in RQ1-1, signal attenuation is very challenging for IIoTsec. For this fact, [PERSON] et al. [36] proposed a novel enhanced quantum PSO (EQPSO) algorithm based on quantum and bio-inspired techniques to regain energy efficiency and achieve optimal routing, reliability, and scalability in an already signally attenuated IIoT ecosystem. When a quantum-based approach was compared with the conventional approach, the former had a record of improved searching precision and convergence swiftness, additional diverse paths generation, and 29.87% throughput improvement beyond the latter. However, the authors only considered optimizing sensors and fog nodes in the IIoT ecosystem. Moreover, [PERSON] et al. [93] discovered another set of vulnerabilities against IIoTsec, such as MITM, photon number splitting (PNS), and faked state attacks. The authors admitted that only MITM had been addressed by a conventional approach. To fill the research gap, a novel chaotic dynamic QKD, multi-state qubit QKD, was implemented to ensure secure communication and distribution of sensitive information in a large-scale industrial network. Also, with the progressive launch and discovery of sophisticated attacks against IIoTsec, in particular, [37] discovered that secure, confidential, and accessible industrial operations could only be guaranteed with the supremacy power of quantum cryptography and QKD applications. The authors even depicted how the quantum technique is far better security-wise than the conventional asymmetric approach, with a reduced dynamic key generation computational overhead. Unfortunately, as good as the secure deployment approach of [93] between the IIoT devices, it does not consider the possible presence of intruders in its evaluation. Moreover, in recognition of the QKD technique as a vital security mechanism of quantum, [PERSON] and [PERSON] [7] proposed and deployed time-sensitive QKD against DDoS, backdoor, injection, ransomware, XSS, and scanning vulnerabilities to ensure a QKD security model across smart industrial operations. QKD has also been proposed for a large-scale networks (LSN) protocol against a common eavesdropping vulnerability in Internet ICSs [35; 73]. In another study of [35], a performance comparison between QLSN and DH key exchange was carried out to minimize risk and maximize reliable communication against a possible showdown occasioned by the eavesdropping effect in an LSN. The result satisfied a QKD-approach effectiveness in a vigorous safety and speedy attack discovery. To validate its viability, the researchers proposed the integration of the QLSN protocol into the IBM Qiskit simulator.
# A Novel Test Rig for the Basic Nonlinear Characterization of Bolted Joints
## 4 Measurement of the Bolt Tension
### Calibration of Strain Gauges  
Before performing dynamical tests, the verification of the strain gauge calibration was carried out by means of a simple tensile test of the bolt (Figure 8a). The bolt was inserted on a metal block, and a lever system was used to multiply the pulling load caused by the application of a dead mass \(M\) at one end. For a certain value of mass \(M\), the expected pulling load at the bolt can be expressed as:  
\[F_{exp}=M\cdot g\cdot L/l \tag{1}\]  
where the two quantities \(L\) and \(l\) are the lengths of the lever arms and the ratio is a multiplication factor of the force produced by the mass \(M\). The actual pulling load indirectly measured from the strain measurement can be written as:  
\[F_{act}=\sigma\cdot A=E\cdot\pi(d/2)^{2} \tag{2}\]  
where \(\varepsilon\) is the strain measured by the Wheatstone bridge, \(E\) is the bolt's material Young's modulus (steel, around 200 GPa), and \(d\) is the screw diameter where strain gauges are applied (13 mm). The results of the tensile test for 5 different values of \(M\) are shown in Figure 8b, from which the linearity and repeatability of the test can be observed. The measured tension preloads well agree with the expected theoretical values, which confirm the accuracy and reliability of the strain gauges application.  
Figure 7: The strain gauges glued on the bolt for the axial tension measurement.
# DNA-PRESENT: An Improved Security and Low-Latency, Lightweight Cryptographic Solution for IoT
## 4 Security, Cost, and Performance Analysis  
The improvements in DNA-PRESENT were evaluated on each of the three cores of the triangle shown in Figure 1--security, cost, and performance--as depicted in Figure 15.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Plaintext** & **Secret Key** & **Ciphertext** \\ \hline
000000000000000 & 0000000000000 & 6 BB502A27 FE7 DAC8 \\ \hline
0000000000000000 & FFFFFFFFFFFFF & 3B88A4E8441280 FE \\ \hline
000000000000000 & 18546B83039E89 EE1 & 784203198F79F29 \\ \hline
FFFFFFFFFFFF & 00000000000000 & 52F220F43D1C838D \\ \hline
FFFFFFFFFFFF &FFFFFFFFFFFF & 541779374B1863C \\ \hline
FFFFFFFFFFFFFFFF & 18546B83039E89 EE & 80595B5E55A422 FF7E \\ \hline
996 ADBEEF0143262 & 00000000000000 & AD1928 EF9 EC07D882 \\ \hline
996 ADBEEF0143262 & FFFFFFFFFFFFFFFF & 45 BC53 ED28 EA9502 \\ \hline
996 ADBEEF0143262 & 18546B83039E89 EE & E77D539 DF778 FF91 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test vectors for DNA-PRESENT algorithm.  
Figure 14: The cipher state after permutation layers.  
These three categories evaluated the algorithm using specific tests and subtests. The datasets for evaluations are adopted from the literature [37; 40; 64; 65; 66], including Low-Density LD (all 0's with at most two 1 bits), High-Density HD (all 1's with at most two 0 bits), and random bits. In this experimental analysis, LDK, HDK, and RK represent Low-Density Key, High-Density Key, and Random Key, respectively, with a 128-bit key length. Meanwhile, LDP, HDP, and RP represent Low-Density Plaintext, High-Density Plaintext, and Random Plaintext, respectively, with 64-bit plaintext length. The experimental environment is kept the same for both algorithms for a fair comparison. Accordingly, the acquired results substantiate the robustness of the proposed methodology.
## 3 Results
### Influence of the Contact Ring Thickness
With regard to the axial distribution of the tangential stress shown in Figure 9, such stress is slightly increased from the center section of the hub (\(z=0\)) up to reaching a maximum value nearby the hub edge and afterward is suddenly decreased to reach the minimum value at the hub edge (\(z=50\) mm). As in the case of the previously discussed for radial stress, the shape of the distributions is similar for the diverse cases of study considering contact rings. However, the following changes are observed: (i) the stress at the hub edge (\(z=50\) mm) is increased as the contact ring thickness is increased, and (ii) the stress at the inner points (\(0<z<40\) mm) is also increased. Thus, it deserves to highlight that the effect of the contact ring on the tangential stress is just the opposite of the effect on the radial stress. Finally, for the von Mises stress (Figure 10), the stress distribution at the interface is slightly increasing from the hub center section (\(z=0\) mm) up to \(z=45\) mm and afterward is suddenly increased, reaching the maximum value at the hub edge (\(z=50\) mm). Figure 10: Radial distributions of the von Mises stress at the interface contact of the ring/shaft for diverse values of the thickness ratio. Figure 9: Axial distributions of the tangential stress at the interface contact of the ring/shaft for diverse values of the thickness ratio. Regarding the effect of the contact ring, it is only noticeable in the high-stress zone nearby the hub edge (\(z>40\) mm) where the stress is progressively reduced as the contact ring thickness is increased in a similar way as it was observed for the radial stress. For inner points (\(0<z<40\) mm) the axial distributions obtained for the diverse contact ring thickness ratios are quite similar and no significant changes are observed.
# Improvement of ID3 Algorithm Based on Simplified Information Entropy and Coordination Degree
## Improved ID3 Algorithm
### The Step of the Improved ID3 Algorithm  
This paper presented a new method that combines the above three steps to build the decision tree. This new method could build a more concise decision tree in a shorter time by combining these three advantages than the ID3 method. The step of the improved ID3 algorithm is summarized in Algorithm 2.  
Figure 5: Different tree structures based on ID3 and the new method: (**a**) the decision tree based on ID3 method; (**b**) the decision tree based on the new method.  
```
Input: decision table \(S=\left\langle U,A\cup D^{l}\right\rangle\) Output: a decision tree
1 Generate node
2 If the training sample inbelong to the same class. Then
3 The node is labeled as leaf node named C; Return
4 End if
5 If\(A=\phi\) or the values in A are same in \(D\), Then
6 The node is labeled as leaf node; Return
7 Else
8 Minimum entropy is chosen as a heuristic strategy to select the optimal partition attribute \(a_{i}\) from A based on the simplified information gain;
9 For every value \(a_{i}^{v}\) in \(a_{i}\)do
10 Generate a branch for node; \(D_{v}\) is the sample subset that has value \(a_{i}^{v}\) from \(a_{i}\) in \(D\)
11 If\(D_{v}\) is empty. Then
12 The node in a branch is labeled as a leaf node; Return
13 Else
14 Forevery branch of \(a_{i}\)do
15 Determine split property by comparing the size of the coordination degree and condition certainty degree.
16 If or there are no other condition attributes, define the branch as a leaf node.
17 ELSE define the branch as a none-leaf node, return Step 8.
18 End if
19 End for
20 End if
21 End for
22 End if
```  
**Algorithm 2**The improved algorithm based on ID3 method
## 2 Numerical Modeling
### CFD Numerical Simulation Method  
Modern wind turbines operate normally at low wind speeds (Mach number does not exceed 0.3), the flow can be regarded as incompressible, and the density of air can be regarded as constant [15]. Moreover, temperature changes are usually not considered in wind turbine simulations, and the equation for conservation of fluid energy can be decoupled from the incompressible Navier-Stokes (N-S) equation, so the vector expression of the N-S equation can be written as:  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot(\rho\mathrm{v})=0 \tag{1}\]  
\[\frac{\partial(\rho\mathrm{v})}{\partial t}+\
abla\cdot(\rho\mathrm{v}\otimes \mathrm{v})=\
abla\cdot\sigma+\mathrm{f}_{b} \tag{2}\]  
where: \(\mathrm{v}\) is the velocity vector, \(\rho\) is the air density, \(\otimes\) is the Crocker operator, \(\mathrm{f}_{b}\) is the body force, \(\sigma\) is the stress tensor; \(\sigma=-pI+T\) represents the sum of the normal and tangential stress, \(I\) is the unit tensor, \(T\) is the viscous stress tensor.  
With the help of numerical solution method, the computational domain is discretized into independent units by finite volume method, and the N-S partial differential equations without analytical solution are converted into discrete algebraic equations on the unit, and then the equation system is closed according to the specific boundary conditions of the problem to be studied and the additional constitutive equations, and the fluid parameters such as velocity and pressure of each node in the calculation domain can be obtained.  
There are several common turbulence models in the RANS method, and the most commonly used turbulence models in wind turbine CFD numerical simulation are briefly introduced below. The SST turbulence model is an evolution of the turbulence model. [PERSON] adds a non-conservative cross-diffusion term on the basis of the Wilcox [16] correction model, so that the model can obtain the same results as the model in some cases, thus effectively mixing the efficiency of the model in the far field and the model in the advantages of robustness and precision in the near-wall region. [PERSON] [17] calls this model the Shear-Stress Transport (SST) model. This model is widely used in the simulation of the aviation industry and boundary layer flow, and the aerodynamics of wind turbine blades are similar to the aerodynamics of airfoils in the aviation industry, which makes the SST model also suitable for studying the aerodynamic performance of wind turbine blades. Therefore, this model was also selected for the CFD numerical simulation in this paper. The SST turbulence model equation is expressed as:  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial}{\partial x_{i}}(\rho ku_{ i})=\frac{\partial}{\partial x_{j}}\left(\Gamma_{k}\frac{\partial k}{ \partial x_{j}}\right)+G_{k}-Y_{k}+S_{k} \tag{3}\]  
\[\frac{\partial(\rho\omega)}{\partial t}+\frac{\partial}{\partial x_{i}}(\rho \omega u  
is the lateral dissipation rate. (interested readers can refer to Ref. [17] for a more detailed description of the SST k-\(\omega\) turbulence model).
## 2 Materials and Methods
### Materials Characterization  
#### 2.2.1 Optical Characterizations  
Different samples of ceria NPs, whether they were pure or had embedded plasmonic nanostructures in-situ/post-added, were optically characterized. The synthesized plasmonic-ceria NPs were characterized based on both their fluorescence emissions intensity and their lifetime using a hand-made fluorescence spectroscopy setup. In this setup, the solution of plasmonic-ceria NPs was exposed to violet excitation from a laser module (Thorlab) with a relatively-narrow emissions spectrum centered at 430 nm. Then, the fluorescent emissions intensity from the synthesized NPs was collected through a perpendicular Newport Cornerstone 130 monochromator for minimum scattering and scanned over the visible spectrum. Then, the detected signal which comes from the monochromator was detected using optical power meter (Newport, 1918R) after being amplified by an Oriel photomultiplier tube (PMT Newport 77360). The amplified emissions intensity was detected by a Newport Power meter 1918R. Based on the emissions intensity of the different plasmonic-ceria NPs, the samples with the highest emissions intensity were characterized through absorbance dispersion measurements using a dual-beam UV-Vis-NIR spectrometer (PG 90+ spectroscopy) over a spectrum between 300-700 nm. Then, a direct bandgap could be calculated from the Urbach-tail results of the absorbance dispersion spectrum. This was done to detect the impact of added plasmonic nanostructures on the bandgap of ceria NPs.
# A Simplification Method for Point Cloud of T-Profile Steel Plate for Shipbuilding
## 4 Point Cloud Simplification Algorithm Based on Curvature Feature
### Simplification Algorithm Based on the Regional Gravity Center  
When the curvature is greater than the given threshold, the simplification algorithm based on the regional gravity center is applied in the proposed method. In the simplified algorithm based on the regional center of gravity, more information features can be reserved than in the random sampling method [33].  
Firstly, where the curvature is larger than the threshold, all points in this region are contained by a cube. Secondly, the cube is divided into a number of small cubes with the same size. Finally, the mean point (regional gravity center) is calculated in each small cube and the nearest point to this mean is calculated.  
Supposing that there are \(N\) points {\(p_{1}\), \(p_{2}\),...., \(p_{N}\)} in the considered box, and there coordinates are (\(p_{ix}\), \(p_{iy}\), \(p_{iz}\)), \(I\) = 1, 2,..., \(N\), then the mean point is marked p. Its coordinates are (\(p_{x}\), \(p_{y}\), \(p_{z}\)), which are given as follows  
\[\left\{\begin{array}{l}p_{x}=\left(\sum\limits_{i=1}^{N}p_{ix}\right)/N\\ p_{y}=\left(\sum\limits_{i=1}^{N}p_{iy}\right)/N\\ p_{z}=\left(\sum\limits_{i=1}^{N}p_{iz}\right)/N\end{array}\right. \tag{12}\]  
Then, the distance \(d_{i}\), \(i\) = 1,2,..., \(N\) between the point p\({}_{i}\) (\(p_{ix}\), \(p_{iy}\), \(p_{iz}\)) in the sub-cube and the mean point p can be calculated, given as follows:  
\[d_{i}=\sqrt{\left(p_{ix}-p_{x}\right)^{2}+\left(p_{iy}-p_{y}\right)^{2}+\left( p_{iz}-p_{z}\right)^{2}} \tag{13}\]  
The distances between other points and the mean point are also calculated. Finally, the nearest point to the mean point in the sub-cube is determined. This point is preserved while deleting other points in the sub-cube. The simplification process is shown in Figure 4.
# GreenRu: A Russian Dataset for Detecting Mentions of Green Practices in Social Media Posts
## 4 Results and Discussion
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{8}{c}{**Practice ID (F1)**} & \multirow{2}{*}{\(\mathbf{F1}_{\text{avg}_{\text{1-avg}_{\text{1-avg}_{\text{1-avg}_{\text{1-avg}_{\text{1-avg}_{\text{1-avg}_{\text{1-avg}_{ \text{1-avg}}}}}}}}}\)} \\  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & \\ \hline \multicolumn{11}{c}{Original dataset} \\ \hline Multi-label & 85.48 & 49.60 & 76.48 & 49.26 & 79.86 & 90.90 & 72.44 & 85.44 & 49.93 & 71.04 & 69.24 & 72.00 \\ One-versus-rest & **87.25** & 75.47 & **77.28** & 88.62 & 76.41 & 92.22 & 84.87 & 84.50 & 49.93 & **79.62** & 78.66 & 82.77 \\ One-versus-rest + w & 86.03 & 77.29 & 69.24 & 83.70 & 83.19 & 92.51 & 82.43 & 85.70 & 49.91 & 78.89 & 78.00 & 82.01 \\ \hline \multicolumn{11}{c}{Augmented dataset} \\ \hline Simple duplication + w & - & 85.28 & 72.29 & 84.95 & 82.81 & **93.38** & 81.51 & 86.60 & 49.93 & - & 79.59\(\uparrow\) & 83.83\(\uparrow\) \\ Simple duplication + w & - & 78.74 & 74.26 & 87.89 & 80.69 & 92.51 & 79.13 & 86.21 & 49.93 & - & 78.67\(\uparrow\) & 82.78\(\uparrow\) \\ \hline Backtranslation & - & 81.14 & 76.65 & 88.97 & **84.84** & 89.51 & 84.70 & 84.65 & **87.46** & - & **84.74**\(\uparrow\) & 84.35\(\uparrow\) \\ Backtranslation + w & - & 78.24 & 75.40 & **89.37** & 81.84 & 90.33 & 83.89 & 85.89 & **87.46** & - & 84.05\(\uparrow\) & 83.57\(\uparrow\) \\ \hline RuGPT3 & - & 79.39 & 74.02 & 85.10 & 82.62 & 90.51 & 83.89 & 84.07 & 49.93 & - & 78.69\(\uparrow\) & 82.80\(\uparrow\) \\ RuGPT3 + w & - & 79.39 & 72.81 & 85.10 & 81.18 & 90.92 & 79.19 & 84.21 & 69.93 & - & 80.34\(\uparrow\) & 81.83 \\ \hline RuT5 & - & 85.28 & 73.80 & 87.73 & 83.45 & 93.07 & 81.81 & 84.12 & 49.93 & - & 79.90\(\uparrow\) & 84.18\(\uparrow\) \\ RuT5 + w & - & **85.89** & 74.55 & 82.97 & 80.42 & 92.64 & 82.78 & 86.17 & 74.96 & - & 82.55\(\uparrow\) & 83.63\(\uparrow\) \\ \hline ChatGPT & - & 84.04 & 76.56 & 88.97 & 83.54 & 90.44 & **85.91** & 84.83 & 83.29 & - & 84.70\(\uparrow\) & **84.90**\(\uparrow\) \\ ChatGPT + w & - & 77.29 & 75.22 & 87.44 & 81.64 & 90.76 & 82.10 & **87.08** & 69.93 & - & 81.43\(\uparrow\) & 83.08\(\uparrow\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results for BERT.