esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
In this study, we propose an effective noncontact emotion recognition system based on RSP signals. The system aims to be deployed in specialized work environments, such as manned submersibles, where emotional changes can lead to decreased performance or even accidents. The emotion recognition accuracy achieved in the study was 68.21%. For all participants, our method achieved an acceptable accuracy rate of over 60% for emotion recognition. The goal of our study is to investigate the feasibility of using millimeter wave radar and breath signals for emotion recognition. We aimed to demonstrate that emotional state detection in a closed-cabin environment can be effectively achieved by combining these two signals. Our results indicate that the model integrating millimeter wave radar and respiration signals shows promising accuracy and feasibility in emotion recognition. This supports our initial hypothesis that this combined approach can enhance the effectiveness of emotion recognition.  
Emotions create a motivational tendency and increase the likelihood of engaging in a range of maladaptive behaviors [45]. By identifying a method that minimally affects the normal operations of personnel and can detect emotional states in closed-cabin environments, specific emotions may be prevented from negatively affecting work tasks. Potential application scenarios for the proposed method include various confined spaces, such as aerospace, automotive, and maritime environments. In these settings, emotions can significantly influence task completion and the safety of both personnel and systems.  
We also note that in the literature [46], similar to our approach, the valence-arousal emotion model is employed. However, this study utilizes a VR scene to induce corresponding emotions and categorizes emotional valence and arousal by collecting EEG signals using a Gradient Boosted Decision Tree. This research includes more detailed emotion classification labels and demonstrates that EEG can effectively reflect neural activation patterns associated with different emotions. In contrast, our study benefits from the radar sensor we used, which exerts less impact and fewer constraints on individuals, functioning effectively in various environmental conditions (e.g., changing light, crowded spaces, etc.). This adaptability allows for a broader range of application scenarios, whereas EEG devices require a complex deployment and setup process, along with significant perceptibility during data acquisition, potentially affecting the final results.  
\begin{table}
\begin{tabular}{c c} \hline
**Feature Acquisition Method** & **Accuracy (\%)** \\ \hline Sparse auto-encoder & 69.34 \\ Manual selected features & 73.35 \\ \hline \end{tabular}
\end{table}
Table 7: Comparison of accuracy using manual selected features with SAE.  
\begin{table}
\begin{tabular}{c c} \hline
**Method** & **Accuracy (\%)** \\ \hline Millimeter-wave radar & 70.12 \\ FaceReader & 31.29 \\ \hline \end{tabular}
\end{table}
Table 8: Accuracy under task-specific lighting condition.
## 5 Mathematical Modeling and the Control of Attitude Dynamics
### Controller Design  
The design of the attitude controller and its gains were based on the limitations in the root locus and difficulties in hardware implementations for higher gain values. Considering these constraints, the specifications for the roll and pitch attitude controllers were defined as shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Phase resistance, R & 0.128 Ohms \\ Phase inductance, L & 0.0000184 H \\ Phase inductance, L & 0.0000184 H \\ Torque constant, \(K_{m}\) & 0.008 Nm/A \\ Back emf constant, \(K_{b}\) & 0.00955 Volts/(rad/s) \\ Rotor inertia, I & \\ About \(x\), \(y\)-axes & \(8.5\times 10^{-6}\) kg\(\cdot\)m\({}^{2}\) \\ About \(z\)-axis & \(4.964\times 10^{-6}\) kg\(\cdot\)m\({}^{2}\) \\ \hline \end{tabular}
\end{table}
Table 1: 1000 KV Brushless DC motor specifications.  
\begin{table}
\begin{tabular}{c c} \hline
**Specifications** & **Roll and Pitch Controllers** \\ \hline settling time & \(\leq\)0.1 s \\ overshoot & \(\leq\)10\% \\ steady-state error & zero \\ \hline \end{tabular}
\end{table}
Table 2: Controller design specifications.  
Figure 9: Roll attitude control loop.
# Asymmetric Encryption of Invisible Structured Light 3D Imaging
## 2 Invisible Structured Light 3D Imaging System
### Encryption Using the Elliptic Curve Encryption Algorithm  
To obtain the phase value of the object, eight fringe images are obtained by irradiating the target plane and the measured object, respectively, with structured light. Following the collections of eight images, the elliptic curve encryption algorithm is used to encrypt the eight images. The eight fringe images generated by the measurement system are used as the plaintext m of the elliptic curve encryption algorithm. Among them, elliptic curve encryption is a key agreement algorithm based on the elliptic curve arithmetic rule of modulo p over a finite field. With the algorithm, the information of the elliptic curve parameters (p, a, b, G, n) is open to users A and B. The elliptic curve equation determined by a and b is \(y^{2}=x^{3}+\text{ax}+\text{b}\), G is the generator of elliptic curve, and n is the order of G. In the simulation experiment, the parameters p, a, b, and G are set to 10,007, 1, 1, and (2, 2), respectively. Then, user A selects ka as its private key and Pa as his public key, where ka is an integer matrix less than n and Pa is the result of ka \(\times\) G. User B selects kb as the private key, and Pb as the public key, where kb is an integer matrix less than n, and Pb is the result of kb \(\times\) G. When user A transmits plaintext m to user B, KA is used to calculate the plaintext m to obtain the encrypted ciphertext c, which is then transmitted to user B, where the encryption key KA is the result of ka \(\times\) Pb and c is the result of m + KA. The elliptic curve encryption flow chart is shown in Figure 2.
## 2 Methodologies and System Description
### Signal Preprocessing
#### 2.3.2 Frequency Domain Transformation  
As shown in Figure 6, the bandpass filtered waveform is transformed into the frequency domain using FFT, and the heart rate is calculated using a formula. The HF and LF frequency bands are filtered out using the bandpass filter, and then transformed into the frequency domain using power spectral density, as shown in Figure 7. HRV parameters are calculated using a formula.  
Figure 4: PSO-ICA waveform plot.  
Figure 5: Band-pass filter waveform plot.  
Figure 6: Fourier Transform waveform diagram.  
Calculating Heart Rate and HRV Parameters  
The measured values need to be converted into values that we can understand through some formulas and algorithms. The following time-domain parameters are defined as Standard deviation of Normal to Normal intervals (_SDNN_) (5) and root mean square of successive differences (_RMSSD_) (6).  
\[SDNN=\sqrt{\frac{\sum_{i=1}^{N}\left(RR_{i}-RR\right)}{N}} \tag{5}\]  
In which \(RR\) is the duration between two consecutive R-peaks of heartbeats, \(N\) is the number of \(RR\) intervals, \(RR_{i}\) is the duration of the \(RR\) interval, and \(RR\) is the mean duration of all \(RR\) intervals.  
\[RMSSD=\sqrt{\frac{1}{N-1}\sum_{i=1}^{N}\left(RR_{i+1}-RR_{i}\right)^{2}} \tag{6}\]  
The definition of frequency domain parameters includes the trapezoidal formula \(f(x)\) (7), as shown in Figure 8, to calculate the area under the power spectral density spectrum in the frequency domain range. The abbreviations of the frequency domain parameters are defined as follows, Heart Rate (\(HR\)) (8), Very Low Frequency (\(VLF\)) represents power within the extremely low frequency range (9), Low Frequency (\(LF\)) represents power within the low frequency range (10), and High Frequency (\(HF\)) represents power within the high frequency range (11).  
\[\int_{a}^{b}f(x)dx=(b-a)\frac{f(a)f(b)}{2} \tag{7}\]  
\[HR=60*\text{frequency} \tag{8}\]  
\[VLF=\int_{0.003\text{ Hz}}^{0.04\text{ Hz}}f(x)dx \tag{9}\]  
\[LF=\int_{0.04\text{ Hz}}^{0.15\text{ Hz}}f(x)dx \tag{10}\]  
Figure 7: Power spectral density plot.  
\[HF=\int_{0.15\,\mathrm{Hz}}^{0.4\,\mathrm{Hz}}\!f(x)dx \tag{11}\]  
This study uses normalized \(HF\), \(LF\), and \(LF\)/\(HF\) ratio for validation, which are defined as follows, normalized low frequency power (\(nLFP\)) (12), normalized high frequency power (\(nHFP\)) (13), and the ratio of low to high frequency power (14).  
\[nLFP=\frac{LF}{LF+HF}\times 100\% \tag{12}\]  
\[nHFP=\frac{HF}{LF+HF}\times 100\% \tag{13}\]  
\[\frac{LF}{HF}=\frac{nLFP}{nHFP} \tag{14}\]
## 4 Model Prediction Results and Analysis
### Parameter Optimization of ANN Model  
With three different structures and three different training methods taken into consideration, different neural network models were developed. As revealed by Table 5, the degree of accuracy exhibited by these models varies. Therefore, for the exceptional case of the average surface roughness prediction, the optimal structure and the best training algorithm were proposed. In the meantime, the corresponding error analysis of the prediction results was conducted. In the surface roughness prediction, the RMSE selection criterion indicated the 3-10-1 neural network structure of BR as an  
Figure 8: Prediction results of genetic-gradient boosting regression tree (GA-GBRT).  
Figure 7: Comparison of training and Test performance between (**a**) before optimization and (**b**) after optimization of the gradient boosting regression tree (GBRT) algorithm.  
acceptable training algorithm. Besides, the comparison results of the three algorithms were discovered to be consistent with the results obtained by [PERSON] [7]. The surface roughness prediction was performed on 10 sets of test data using BR, as shown in Figure 9. The prediction error increased with the rise of the feed, which was caused by the cutting force and machine tool vibration.
# Numerical Study on Transient Annular Pressure Caused by Hydration Heat during Well Cementing
## 2 Transient Model of Annular Pressure Caused by Hydration Heat during Well Cementing
### Annular Pressure Build-Up Caused by Thermal Expansion  
As a result of the cement hydration heat, an obvious temperature increase in the cement slurry can be observed. The cement slurry is trapped in the annulus between the casing and the wellbore, which results in thermal expansion and the annular pressure build-up. The annular pressure build-up can be affected by two factors: (1) the change in temperature in the annulus; (2) the change in volume of the annulus. Thus, the prediction model of annular pressure build-up during well cementing can be expressed as:  
\[\Delta P_{a}=\frac{\alpha_{l}}{k_{T}}\Delta T-\frac{\pi}{k_{T}V_{ann}}\Big{[}2 ur_{co}-u^{2}\Big{]}L \tag{19}\]  
where \(\alpha_{l}\) is the thermal expansion coefficient of fluid, \({}^{\circ}\)C\({}^{-1}\); \(k_{T}\) is the isothermal compressibility coefficient of fluid, MPa\({}^{-1}\); \(V_{ann}\) is the volume of annulus, m\({}^{3}\); \(u\) is the radial displacement of casing, m; \(L\) is the length of casing, m.  
The first item in Equation (19) is the annular pressure build-up caused by fluid expansion, and the second item in Equation (19) represents the annular pressure change caused by the annulus volume change.  
The pressure build-up in the annulus can result in the radial deformation of the casing, which changes the volume of the annulus. According to the thick-walled cylinder theory [17], the radial displacement under pressure can be expressed as:  
\[u=\frac{1+\mu}{E}\Bigg{[}\frac{(1-2\mu)r_{co}^{3}+r_{ci}^{2}r_{co}}{r_{co}^{2} -r_{ci}^{2}}P_{c}-\frac{(1-2\mu)r_{ci}^{2}r_{co}+r_{ci}^{2}r_{co}}{r_{co}^{2}- r_{ci}^{2}}P_{a}\Bigg{]} \tag{20}\]  
where \(\mu\) is the Poisson's ratio of the casing; \(E\) is the elastic modulus of the casing, MPa; \(r_{co}\) is the outer diameter of the casing, m; \(r_{ci}\) is the inner diameter of the casing, m; \(P_{c}\) is the pressure in the casing, MPa; \(P_{a}\) is the pressure in the annulus, MPa.
## 4 COIN-LEO Algorithm
### Deep Reinforcement Learning Using the PPO Algorithm
#### 4.2.1 State  
In the reinforcement learning model, the state set S is composed of multiple state variables, which are used to describe the current situation of the environment. Specifically, the state set S contains the following state variables: S : \(\{\overset{\rightarrow}{c},\overset{\rightarrow}{m},\overset{ \rightarrow}{l},\overset{\rightarrow}{b},\text{p},\text{i}\}\). Among these state variables, \(\overset{\rightarrow}{c}\) and \(\overset{\rightarrow}{m}\) describe the resource status of each satellite in the cluster, specifically; \(\overset{\rightarrow}{c}\) and \(\overset{\rightarrow}{m}\) represent the remaining CPU and memory resources on each satellite; \(\overset{\rightarrow}{l^{l}}\) and \(\overset{\rightarrow}{b}\) describe the transmission status of inter-satellite links; and p and i are used to track information about the previous splitting point and the current step, respectively. These state variables together form the state set S of the environment. In reinforcement learning, the agent observes the current state S to make decisions and choose the optimal action.
# Tomato Leaf Disease Classification by Combining EfficientNetv2 and a Swin Transformer
###### Abstract  
Recently, convolutional neural networks (CNNs) and self-attention mechanisms have been widely applied in plant disease identification tasks, yielding significant successes. Currently, the majority of research models for tomato leaf disease recognition rely solely on traditional convolutional models or Transformer architectures and fail to capture both local and global features simultaneously. This limitation may result in biases in the model's focus, consequently impacting the accuracy of disease recognition. Consequently, models capable of extracting local features while attending to global information have emerged as a novel research direction. To address these challenges, we propose an Eff-Swin model that integrates the enhanced features of the EfficientNetV2 and Swin Transformer networks, aiming to harness the local feature extraction capability of CNNs and the global modeling ability of Transformers. Comparative experiments demonstrate that the enhanced model has achieved a further increase in training accuracy, reaching an accuracy rate of 99.70% on the tomato leaf disease dataset, which is 0.49-3.68% higher than that of individual network models and 0.8-1.15% higher than that of existing state-of-the-art combined approaches. The results show that integrating attention mechanisms into convolutional models can significantly enhance the accuracy of tomato leaf disease recognition while also offering the great potential of the Eff-Swin backbone with self-attention in plant disease identification.  
tomato leaf disease classification; Swin Transformer; CNN; self-attention; local and global features +
Footnote †: journal: Applied sciences
# Recognition and Location Algorithm for Pallets in Warehouses Using RGB-D Sensor
## 3 Algorithm Performance Test
### Accuracy Test in Static Experiment  
Accuracy and time consumption are important indicators for a pallet recognition algorithm. An experiment was designed to test the relationships between detection accuracy, pallet distance, and angle. This experiment was conducted in a room to easily control the environmental conditions. The sensor and pallet were set as shown in Figure 1. The pallet was moved to change the distance \({}^{C}z\) and the angle \(\theta\) after fixing the sensor. The result was transformed into [W] space with the pose of the sensor.  
The distance \({}^{C}z\) values were chosen as 1000, 2000, 3000, and 4000 mm because the detection range of the depth sensor is 500\(-\)4500 mm. \(\theta\) was chosen as 0, \(\pm\)5\({}^{\circ}\), \(\pm\)10\({}^{\circ}\), \(\pm\)15\({}^{\circ}\), \(\pm\)20\({}^{\circ}\), and \(\pm\)25\({}^{\circ}\). The estimation results and errors obtained by the algorithm were recorded and counted. The overall computation time and the computation time of the main processes of the algorithm--classifying the source image, creating the template, compressing, matching, extracting ROI, and estimating pallet parameters--were also recorded.  
The proposed algorithm was compiled based on OpenCV 3.0, running on a PC with 16 GB of RAM, an Intel Core i7-6820 processor, and a Windows 10 operating system. The sensor was a Kinect 2 (Microsoft, America), which can obtain RGB images with a resolution of 1920 \(\times\) 1080 pixel and depth images with a resolution of 512 \(\times\) 424. Kinect 2 detects the distance based on the time of flight, the depth detection range is 0.5-4.5 m and detection accuracy is \(\pm\)4 mm. The depth image size was 512 \(\times\) 424. The pallet size was 1300 \(\times\) 1300 \(\times\) 150 mm.
# SNR Analysis for Quantitative Comparison of Line Detection Methods
## 3 Method--SNR of ED and SD for Line Detection
### Measure of Signal Strengths  
Edge strengths are measured at the boundaries of smoothed line model in terms of the values of first and second derivatives. The first derivative indicates the gradient of the smoothed edge profile and is derived by substituting \(x=L-\frac{w}{2}\) in Equation (9) as follows:  
\[\mathbf{F}^{\prime}_{\mathbf{b}\ast\mathbf{s}}|_{x=L-\frac{w}{2}}=\frac{k}{ \sqrt{2\pi(\sigma_{b}^{2}+\sigma_{g}^{2})}}\big{[}1-e^{-\frac{w^{2}}{2(\sigma _{b}^{2}+\sigma_{g}^{2})}}\big{]}. \tag{11}\]  
To distinctly determine an edge pixel in the local areas in an image in a non-maxima suppression process, the absolute values of the gradients at its neighboring pixels should be sufficiently lower than the absolute values at the edge location, \(x=L-\frac{w}{2}\). Thus, the absolute value of the second derivatives of the neighborhood pixels should be high. Therefore, the neighborhood pixel, which is one pixel away from the edge location, is deleted. Moreover, the second derivative at the pixel location \(x=L-\frac{w}{2}-1\) is derived as another measure of edge strength from Equation (10) as follows:  
\[\mathbf{F}^{\prime\prime}_{\mathbf{b}\ast\mathbf{s}}|_{x=L-\frac{w}{2}-1}= \frac{k}{\sqrt{2\pi}(\sigma_{b}^{2}+\sigma_{g}^{2})^{\frac{3}{2}}}\big{[}e^{ -\frac{1}{2(\sigma_{b}^{2}+\sigma_{g}^{2})}}\quad-e^{-\frac{(1+w)^{2}}{2( \sigma_{b}^{2}+\sigma_{g}^{2})}}(1+w)\big{]}. \tag{12}\]  
Alternatively, to distinctly determine a line pixel in a non-maxima suppression process, the absolute values of the first derivatives or the gradients of the neighborhood pixel of the line pixel should be high. Therefore, as one of the neighborhood pixels, a pixel located at \(x=L-1\) is selected. The first derivative of this pixel, as a measure of line strength, is derived from Equation (9) as follows:  
\[\mathbf{F}^{\prime}_{\mathbf{b}\ast\mathbf{s}}|_{x=L-1}=\frac{k}{\sqrt{2\pi( \sigma_{b}^{2}+\sigma_{g}^{2})}}\big{[}e^{-\frac{(1-\frac{w}{2})^{2}}{2(\sigma _{b}^{2}+\sigma_{g}^{2})}}-e^{-\frac{(1+\frac{w}{2})^{2}}{2(\sigma_{b}^{2}+ \sigma_{g}^{2})}}\big{]}. \tag{13}\]  
Moreover, for detecting a line pixel, its second derivative at the line location \(x=L\) must be high, which can be derived from Equation (10) as follows:  
\[\mathbf{F}^{\prime\prime}_{\mathbf{b}\ast\mathbf{s}}|_{x=L}=-\frac{kw}{\sqrt{ 2\pi}(\sigma_{b}^{2}+\sigma_{g}^{2})^{\frac{3}{2}}}e^{-\frac{w^{2}}{8(\sigma_ {b}^{2}+\sigma_{g}^{2})}}. \tag{14}\]
# Sparse Data-Extended Fusion Method for Sea Surface Temperature Prediction on the East China Sea
## 1 Introduction
Machine learning is an important method to realize artificial intelligence. It is a method of using algorithms to analyze and learn from data, and then make decisions and predictions about phenomena in the real world. As a technology for realizing machine learning, deep learning has the advantage of proposing a method for the computer to automatically learn pattern features, and integrating feature learning into the model building process. It reduces the incompleteness caused by man-made design features and has more advantages compared with OI, 4 DVAR, and KF, conceptually. At present, machine learning has a very wide range of applications [16; 17; 18], such as language recognition, image recognition, data mining, and expert systems, etc. Some machine learning algorithms are used in the assimilation of ocean remote sensing data, such as genetic algorithms [19] and neural networks [20]. These data fusion methods have shown certain advantages in different aspects. However, they only assimilate data of type and homology. In addition, the past statistical methods rely more on regression analysis for spatial modeling, and the model input parameters are relatively single. To tackle the challenges and issues mentioned above, this paper proposed a new framework for SST prediction, where the gate recurrent unit (GRU) [21] is employed to classify data according to the characteristics of remote sensing data. Then the sparse measurement point observation data in the same category is used to fuse with the large-scale remote sensing data, resulting a temperature background field with promising accuracy of prediction. The rest of this paper is organized as follows: Relevant definitions involved in this work are introduced in Section 2. The whole process of proposed framework for SST prediction, including: (1) the SST correlation calculation method based on deep learning,(2) the heterogeneous clustering method, (3) the sparse data expansion and fusion method, and (4) the SST prediction method based on deep learning, is described in detail in Section 3. Section 4 presents the experimental results and discussions. Finally, conclusions and future prospects are summarized in Section 5.
## 3 Results  
### Reflectance Analysis  
Figure 4 shows the average spectral reflectance of U and I seedlings collected at five months after inoculation and standard deviations for each wavelength. Based on the figure, the U seedling established higher reflectance in the green and NIR spectrum at 520 to 570 nm and 750 to 950 nm, respectively. Maximum differences between U and I seedlings were detected in the NIR with values around 12% to 16%. It should also be noted that the NIR reflectance can also distinguish between U and I seedlings without any overlapping wavelength, even though it has higher standard deviations than the green reflectance.  
Table 3 tabulates 35 significant bands of U and I reflectance spectra based on the work of [PERSON] et al. [35]. These significant bands accounted for 30% of the 125 bands that were confirmed to be statistically significant. Including only 35 significant bands instead of 125 bands could prevent analytical problems due to excessive bands, which would make the applications in-field less complex, and cost-effective [45].  
Figure 4: Spectral reflectance at U and I treatments. Each value represents a mean of 540 and 582 sample points of U and I, respectively. Vertical bars represent standard deviation.