esa-sceva/satcom-chunk-collection huggingface dataset:
# Aerodynamics and Motion Performance of the H-Type Floating Vertical Axis Wind Turbine
## 5 Conclusions  
The surge-heave-pitch coupling nonlinear motion equations of the H-type floating VAWT were established considering the coupling between aerodynamic and hydrodynamic loads, and the motions of a 5 MW H-type floating VAWT were studied. The motion of the H-type floating VAWT was also compared with that of the \(\Phi\)-type floating VAWT; they have the same floating foundation and total displacement. The main conclusions are as follows:  
1. The natural periods of surge, heave, and pitch motions of the H-type floating VAWT are 51.2 s, 20.5 s, and 29.3 s, respectively. The small STDs of the LC3 and LC4 show the good motion performance of the H-type floating VAWT in the wave and wind conditions.
2. The effects of wind and wave loads on the motions of the H-type floating VAWT are assessed. For LC3, the motion frequencies of the floating VAWT are dominated by the wave frequencies, and there are small components of 2P frequency for the pitch motions. The 2P frequency is more significant in irregular wave conditions (LC4).
3. The effects of the floating foundation motions on the aerodynamics are also assessed. The results show that the normal force is much bigger than the tangential force. Compared with the fixed foundation case, the amplitudes of normal and tangential forces increase up to 9.4% and 16.3%, respectively, for the floating foundation case. Besides the 2P frequency, the frequencies of aerodynamics also include the wave frequency and the difference frequency between 2P and wave frequency for the floating foundation case. The effect of the floating foundation motion on the aerodynamics cannot be ignored.
4. The H-type VAWT withstand smaller wind loads compared with the \(\Phi\)-type VAWT, and mean values of the H-type floating VAWT's motions are much smaller compared with the \(\Phi\)-type floating VAWT. The STDs of motions of the two floating VAWTs are almost same, as they mainly depend on the wave loads.  
The project was supported by the Natural Science Foundation of China (No. 51579176), the Natural Science Foundation of Tianjin (No. 16 JCYBJC21200), and the Fund of the State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University (No. 1501).  
Figure 17: Mean generator power of two floating VAWTs (cases LC5).  
**Author Contributions:** [PERSON] and [PERSON] deduced the formulae and carried out numerical calculation; [PERSON] analyzed the data; and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Review of Internet of Things Technologies for Ambient Assisted Living Environments
## 5 Discussion
### Technological Potential  
Innovative and novel methods, technologies, services and products to improve the quality of life of inhabitants and users of intelligent AAL environments:  
* Novel methods and technologies for very precise indoor positioning of older people or impaired inhabitants using wireless local area networks (WLAN) [151], Bluetooth beacons [152], radio and ultrasound signals.
* Innovative non-invasive and non-obtrusive methods for real-time human state and environment monitoring, posture identification while aiming for better precision, which can be implemented using standard/existing equipment and are much cheaper, less intrusive and more comfortable and affordable (e.g., using smartphone technology [153; 154])
* Innovative personalized services and decision support systems [155] based on artificial intelligence (AI) methods aimed to o adapt to each home resident individually; to learn autonomously, without a priori information about the lifestyle and habits of the residents; to follow changing habits of residents and operate according to the resident's states and parameters of the environment; to make home control decisions without any additional intervention from the residents; and to provide a much higher level of comfort, safety and functionality, which cannot be achieved by any present technologies and methods.
* Novel development methodologies for the multi-agent intelligent AAL systems and services that allows the creation of dynamic, easily extended, and scalable solutions and do not require technical knowledge on the underlying technologies [156].  
* Internet integration into relatively small devices, that could be integrated into currently still not computerized systems (e.g., household devices for smart house deployment and/or energy saving) or implanted into human body so as to increase mobility and quality of life of disabled people [157; 158; 159];
* Possibility for deployment of cheap geographically distributed and scalable network-centric infrastructure and middleware for AAL based on cloud computing and IoT (e.g., for monitoring passenger flows in transport, observing weather conditions for climate control, etc.) [160].
## 2 Degradation of Materials in Space--Stability Issues on Mirrors
### Ultraviolet Radiation
After an investigation on flight witness mirrors, they concluded that a non-qualified process was employed in the production of the flight mirrors, which inadvertently caused the deposition of a thin layer of tungsten oxide, WOx, on the surface of the mirrors. Tungsten oxide, when illuminated with UV, becomes absorptive in the near infrared with a spectral dependence that compared very well with the inferred behavior of the mirrors on orbit.
# Research on Fault Diagnosis Method for Rotating Machinery Based on Edge Computing
## 5 Experimental Validation
### Analysis and Discussion  
To validate the effectiveness of the proposed method, this study analyzed the decision-level fusion fault diagnosis method based on the convolutional neural network (CNN) integrated with D-S evidence theory for multidimensional feature data from various perspectives. Firstly, it was verified that modifying the input layer for vibration data increased the diagnostic accuracy by 3.2%. Secondly, the CNN fully connected layer was optimized by incorporating the D-S evidence theory method. This approach combines the advantages of CNN feature extraction with the flexibility of D-S evidence theory, resulting in a further 2.35% increase in diagnostic accuracy. The reason for this result is that the dual-modal data combine information from different sources (vibration signals and temperature signals), providing more dimensions and more comprehensive information for fault diagnosis. Subsequently, the proposed method was compared with other machine learning methods, and its performance was analyzed in terms of average accuracy, rapidity, and stability. The results demonstrate that the CNN-DS method proposed in this study offers significant advantages and shows the greatest potential for motor fault diagnosis using dual-modal data. Finally, the proposed algorithm was embedded into the edge node, and the running speed of the algorithm on both the edge node and the cloud was analyzed. The results confirmed that deploying the algorithm on the edge node can improve the fault diagnosis speed.  
Based on the experimental results of this study, it can be observed that the CNN-DS algorithm proposed in this paper has lower recognition rates for cage fault (FIF) and rotor imbalance fault (Unbalance). The possible reasons for this are the similarity between cage faults and rotor imbalance faults, as well as the interference of noise during the data collection process. In future work, we will further analyze the impact of different  
Figure 13: Evaluation of CNN-DS accuracy on edge and cloud.  
noise interferences on model performance, determine the noise threshold, and clarify the limitations of its practical application.
## 2 Setup and Modeling
### Modeling
#### 2.2.5 Solution  
Equation (4) is solved using [PERSON]'s numerical method, with the initial conditions \(h(t=0)=\Gamma aN_{0}[(I_{dc}/I_{0})-1]L_{RSOA}\), to calculate the electric field of the encoded signal at the RSOA output from (3). The knowledge of \(E_{RSOA}(t)\) and of \(T_{BFL}(\lambda)\) from (5) allows us to find \(E_{BFL}(t)\) from (7). This process is conducted for the optical input, electrical data, RSOA and BFL default parameters values listed in Table 1.  
In particular, the value of the RSOA carrier lifetime is extracted by setting the 3 dB cut-off frequency of the RSOA E/O response, which is given by the following closed-form expression [23]  
\[f_{3 dB}=\frac{\sqrt{3}}{2\pi T_{car}}\Bigg{\{}1+W\bigg{[}\frac{2P_{CW}}{P_{sat}} \exp\bigg{(}2\delta_{0}L_{RSOA}+\frac{2P_{CW}}{P_{sat}}\bigg{)}\bigg{]}\Bigg{\}} \tag{9}\]  
where \(W\)[.] is [PERSON]'s function available in Matlab [17; 29] and \(g_{0}=\Gamma aN_{0}\{[(l_{dc}-I_{m})/I_{0}]-1\}\) is the RSOA steady-state gain coefficient, equal to the measured one, 0.89 GHz, then solving for \(T_{car}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Symbol** & **Definition** & **Value** & **Unit** & **Reference** \\ \hline \(P_{CW}\) & CW input power & \(-5\) & dBm & [21] \\ \(\lambda_{enc}\) & Encoded signal wavelength & 1560 & nm & [21] \\ \(T_{per}\) & Electrical data pulse repetition period & 250 \& 1000 & ps & [21] \\ \(t_{r}\) & Electrical data pulse rise time & 17\% of repetition period & ps & [21] \\ \(I_{m}\) & RSOA modulation current & 20 & mA & \begin{tabular}{c} Fitting \\ (steady-state) \\ \end{tabular} \\ \(I_{dc}\) & RSOA dc bias current & 70 & mA & \begin{tabular}{c} Fitting \\ (steady-state) \\ \end{tabular} \\ \(I_{0}\) & RSOA transparency current & 45 & mA & \
\begin{tabular}{c} Fitting \\ (steady-state) \\ \end{tabular} \\ \(\alpha_{LEF}\) & RSOA linewidth enhancement factor & 5 & - & [28] \\ \(\Gamma\) & RSOA confinement factor & 0.17 & - & [28] \\ \(a\) & RSOA differential gain & \(6.9\times 10^{-20}\) & m\({}^{2}\) & [28] \\ \(N_{0}\) & RSOA carrier density at transparency & \(5.8\times 10^{-23}\) & m\({}^{-3}\) & [28] \\ \(n_{g}\) & RSOA semiconductor material group & 3.6 & - & [28] \\ \(L_{RSOA}\) & RSOA active region length & 713 & um & [28] \\ \(T_{car}\) & RSOA carrier lifetime & 408 & ps &
\begin{tabular}{c} Fitting \\ (dynamic) \\ \end{tabular} \\ \(P_{sat}\) & RSOA semiconductor material saturation power & 3.2 & dBm & [28] \\ \(B\) & BFL birefringence & \(3.3\times 10^{-4}\) & - & [21] \\ \(L_{BFL}\) & BFL PM total length & 8.5 & m & [21] \\ \(\Delta\lambda\) & BFL detuning & \(-0.08\) & nm & [21] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters default values.
# Superpixel Segmentation of Hyperspectral Images Based on Entropy and Mutual Information
## Dimensionality Reduction of HSIs Based on Information Measures
### Band Selection Algorithm Based on Mutual Information  
The dimensionality reduction of the HSIs was achieved by optimizing two criteria, i.e., maximizing the amount of information and minimizing the redundancy. These two criteria need to be considered simultaneously.  
[PERSON] et al. used the normalized mutual information for band selection [27]. In this paper, the \(kth\) order normalized information (NI) of the bandset \(S=\{B_{1},\cdots,B_{k}\}\) was calculated as follows:  
\[Nl_{k}(S)=\frac{K\times I(S)}{\sum\limits_{i=1}^{k}H(B_{i})} \tag{8}\]  
where \(I(S)\) is the mutual information from \(B_{1}\) to \(B_{k}\), and \(H(B_{i})\) is the entropy of \(B_{i}\).  
From the above step, three segments can be obtained, i.e., \(\text{Set}_{R}^{t}\), \(\text{Set}_{\text{B}}^{t}\), and \(\text{Set}_{\text{G}}^{t}\). When the value of the mutual information between three bands \(Nl_{3}(\text{S})\) is smaller, the amount of useful information contained in the selected spectral bands is larger, and the dimensionality reduction effect is better. Thus, the minimum, second minimum, and up to \(nth\) minimum values of \(Nl_{3}(\text{S})\) can be calculated based on the values of \(x_{Rn},y_{Rn},z_{Gn}\Big{(}x_{Rn}\in Set_{R}^{t},y_{Rn}\in Set_{B}^{t},z_{Gn }\in Set_{G}^{t},n=1,2,\ldots 10\Big{)}\). Finally, the \(3n\) spectral bands are selected to composite the false color images. This method is not only convenient to configure, but also produce the images containing the main spectral information. The optimal value of \(n\) was determined by experiments.
###### Abstract  
Several precedents support an association between single nucleotide genetic polymorphisms (SNPs), the obese phenotype, and eating behavior in the infant-juvenile population. This study aims to study this aspect in depth, analyzing the eating behavior of a sample of schoolchildren from Madrid in regard to their genetic predisposition to obesity. A total of 258 schoolchildren, aged 6 to 16 years, were evaluated through the Children's Eating Behaviour Questionnaire (CEBQ) and the genotyping of 32 SNPs. Associations were observed between the total genetic risk score and eating behaviors related to emotional eating and food responsiveness. Individually, different SNPs were associated with eating behaviors, primarily those related to pro-eating behaviors or increased risk of developing obesity. However, diverse results are obtained, depending on the SNP. These results highlighted the strongest associations between the rs1801725 SNP risk allele (CASR) and increased enjoyment of food by 1846-fold. Likewise, the satiety response was associated with SNP rs11676272 (ADCY3) 2.39 and SNP rs7566605 (INSIG2), increasing this response by 2.39 and 1.63 times, respectively. Emotional anti-ingesting behaviors were inversely associated with SNP rs1421085 (FTO) and SNP rs987237 (TFAP2B). In contrast, SNP rs55915917 (CRHR1) increased the risk of these behaviors. SNPs rs4788099 (SH2B1), rs6857 (NECTIN2), and rs180172 (ADCY3) were associated with slow feeding. In conclusion, associations were found between most of the analyzed SNPs and the CEBQ items. This suggests that feeding behavior exists as a mediator between genotype and obesity phenotype, beginning in childhood.  
appetite; satiety response; feeding behavior; pediatric obesity Article
# Brake Torque Sensor Calibration Device
## 4 Calibration Equipment Models
### Design with the Angular Gearbox  
With this design, the problematic and time-consuming settings of the torque were eliminated. This was achieved by replacing the threaded rod with an angle gear with a given gear ratio. The advantage is an easier and faster setting of the torque value, which could be set in both directions of rotation. The disadvantages include the problematic axial adjustment of the torque sensor to the standard and the introduction of errors due to the effect of rolling bearings. By using the gearbox, the built-in dimensions of the entire calibration device were increased, which significantly increased the entire weight of the device. A rather complicated gearbox with a high gear ratio and a self-locking mechanism meant that this device was not developed further. Figure 6 shows the model of this device.  
The determination of torque \(M_{2}\) requires knowledge of torque \(M_{1}\). The wheel has a radius (rm) and includes a crank. An operator causes the force, Fm. Then, the torque in the gearbox input \(M_{1}\) is given by the following formulation:  
\[M_{1}=F_{m}\cdot r_{m} \tag{4}\]  
where  
\(M_{1}\) denotes the torque on the gearbox input [Nm].  
\(F_{m}\) denotes the force induced by an operator [N].  
\(r_{m}\) denotes the wheel radius [m].  
The gear ratio of gearbox \(i\) can be calculated based on the input and output angular velocities, \(\omega_{1}\) and \(\omega_{2}\), as follows:  
\[i=\frac{\omega_{1}}{\omega_{2}} \tag{5}\]  
Then, torque \(M_{2}\) is given by the following equation:  
\[M_{2}=M_{1}\cdot i\cdot\eta \tag{6}\]  
where  
\(\eta\) denotes the mechanical efficiency of the gearbox.  
Substituting Formulae (4) and (5) to Equation (6), torque \(M_{2}\) is calculated as follows:  
\[M_{2}=F_{m}\cdot r_{m}\cdot\frac{\omega_{1}}{\omega_{2}}\cdot\eta \tag{7}\]
## 3 Methodology
### Data Pre-Processing  
The pre-processing steps involved several stages to prepare the images for analysis and interpretability. Initially, the heat gradient files were converted into an image format to ensure consistency and compatibility with standard image processing techniques. Next, experts manually segmented the full thermogram images into the chest region, a step included with the dataset. Subsequently, previously segmented grayscale images were cropped to eliminate extraneous regions along each axis. Before feature extraction, the grayscale version of the input image was resized to a standardized dimension of 240 \(\times\) 240 pixels to ensure uniformity. Post-feature extraction, an optional normalization, and a rounding step were included, where the features were scaled based on a precomputed scaler from a training dataset. Finally, the selected features were fed into downstream machine learning models for classification.
# Research on Short-Term Prediction Methods for Small-Scale Three-Dimensional Wind Fields
## 3 ConvLSTM-DSConvGRU Hybrid Prediction Model  
We have seen that the key to the coupling of WRF and CFD to simulate the small-scale three-dimensional wind field is to interpolate the output data of the WRF model to the inflow boundary of the CFD-simulated domain. Moreover, the results in Section 2.3 show that the inverse distance weighted interpolation method is more suitable for the simulation and prediction of the three-dimensional wind field in the simulated domain of this study than the nearest neighbour interpolation method. The subsequent focus of this study was to construct a neural network prediction model to accurately predict the WRF output data as the CFD inflow boundary conditions. This study introduces the ConvLSTM model and a separable convolution to reconstruct the ConvGRU model as the DSConvGRU model and establishes a ConvLSTM-DSConvGRU hybrid prediction model based on an encoder-decoder architecture.
# 3D Photoacoustic Tomography System Based on Full-View Illumination and Ultrasound Detection
## 3 Results
### Performance Test of the 3D-PACT
#### 3.1.3 The Superiority of Photoacoustic Coplanar Configuration and Virtual Point Detection
The \(PSNR\) of the photoacoustic signal from the dual-foci virtual point ultrasound transducer was 35.9379 dB, which was much larger than that of the conventional unfocused transducer (15.2504 dB), indicating that the dual-foci virtual point transducer had a higher detection sensitivity. Moreover, the high detection sensitivity of the photoacoustic tomography system is further illustrated in Figure 6e, in which the blue and red A-line plots corresponding to the raw photoacoustic signal with the dual-foci virtual-point ultrasonic transducer and the traditional unfocused ultrasonic transducer are depicted, respectively. The two A-line plots were randomly obtained under the premise of the same scanning position and sampling points. It can be seen that the photoacoustic signal from the dual-foci ultrasonic transducer had an amplitude increase of nearly 2.5-fold, which greatly improved the signal to noise ratio of the photoacoustic image. Figure 6f shows the absorption intensity at the position of the centerline of the reconstructed image, and it can be found that the absorption intensity of the center line position of the photoacoustic image obtained by the unfocused ultrasonic transducer was weak (corresponding to the black dotted line in the Figure 6f), and there are signs that the signal was flooded by noise. In addition, the virtual-point ultrasonic transducer has a large receiving angle, which makes the photoacoustic tomography under sparse sampling possible. In the experiment mentioned above, although the number of sampling points per cross section was 90, the system with virtual-point \begin{table}
\begin{tabular}{c c c c} \hline
**Index** & \(PSNR\) (dB) & \(MSE\) & \(SSIM\) \\ \hline PA image (c) (Dual-foci transducer) & 35.9379 & 0.0051 & 0.9995 \\ PA image (d) (Unfocused transducer) & 15.2504 & 0.0350 & 0.9174 \\ \hline \end{tabular}
\end{table} 
Table 2: Quality evaluation of reconstructed images. detection could still perform high quality photoacoustic tomography. However, there were some artifacts in the photoacoustic image under the traditional unfocused transducer. The experiment proved that the system proposed in this paper can improve the imaging speed and reduce the burden of data acquisition device using sparse sampling. The comparative experiments analyzed the photoacoustic signal acquisition mode based on photoacoustic coplanar structure and virtual-point ultrasound detection from a quantitative point of view.
## 3 Conventional Detection Scheme  
To remove the impact of the channel on the detection performance, the works developed in [21; 22; 23; 24; 25; 26; 27] perform differential correlation, wherein the effect of STO is mitigated together during non-coherent detection. Based on the assumption that the \(l\)-th transmitted symbol \(X_{l}(k)\) is equal to \(P_{i}(k)\) and \(C_{l}(k)\approx C_{l}(k-1)\), the differential correlation between neighboring subcarriers of the received PSSS sequence can be expressed as  
\[\begin{split}\bar{Y}_{l}(k)&=Y_{l}(k)Y_{l}^{*}(k-1) \\ &\approx|C_{l}(k-\epsilon)|^{2}D_{i}(k-\epsilon)e^{j2\pi\tau/N}+ \bar{Z}_{l}(k),\ k\in\mathcal{D},\end{split} \tag{8}\]  
where \(a^{*}\) denotes the complex conjugate of \(a\), \(|\cdot|\) denotes the magnitude operation, \(D_{i}(k)=P_{i}(k)P_{i}^{*}(k-1)\), \(\bar{Z}_{l}(k)\) denotes the contribution to AWGN, and \(\mathcal{D}=\{k|-N_{p}/2+1\leq k\leq-1,2\leq k\leq N_{p}/2\}\). As benchmark, two representative conventional schemes are considered in this paper. One is a sequential IFO and SI detection scheme [27] and the other is a low-complexity joint detection method [23].
The drawback in the above literature is quite evident because problems that occur on a real plant floor, such as unusual operations of control, are neglected. Only a few studies have discussed and analyzed control applications for rotary dryers in a real industrial environment [10]. Consequently, there is a lack of specific research dedicated to the implementation of intelligent controllers in a real industrial rotary dryer. The main purpose of this paper is to describe and examine the design, as well as the applications, of a new control strategy to an industrial rotary dryer. We will illustrate the practical aspects and details to design our controller based on the rotary dryers used to dry cut tobacco. Actually, our proposed controller operates in a layer above one of the basic regulatory control which is already in place in the plant. This study will further show that the new controller can maintain the same quality the product, as already provided by manual operation, with an increase in the reduction of over-dried cut tobacco. The paper is organized as follows: Section 2 deals with the description of the cut tobacco drying process; as well as the current regulatory control system. Section 3 illustrates the proposed intelligent control system. Section 4 explains the experimental setup and the result analysis. Section 5 shows the conclusions.