esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Patient Cohort  
Images of 198 patients were retrieved. In 32 cases, the early WB scan was not available, so it was not possible to compute \(\mathrm{Hr}\) and \(\mathrm{WBr}\), while in 12 cases, other clinical variables were missing. These 44 patients were excluded from the dataset for the development of the LASSO regression model and the NN model, but when \(\mathrm{Hr}\) and \(\mathrm{WBr}\) were available, they were included in the comparison of populations with different ATTR etiology. Among the 154 patients included in the development of the computational models, there were 100 cases of ATTRwt and 54 cases of ATTRm with specified TTR mutated genes (Supplementary Material Table S1).  
The characteristics of the 154 patients included in the dataset for LASSO regression, RF, and NN are reported in Table 1. The reported \(\mathrm{Hr}\), \(\mathrm{WBr}\), and \(\mathrm{H}\)/\(\mathrm{WBr}\) are the ones obtained with the GM method and the homemade assisted workflow.  
\begin{table}
\begin{tabular}{l l} \hline
**Variable** & \multicolumn{2}{l}{**Occurrences (Percentage)**} \\  & **or Median [Range]** \\ \hline
**Patient characteristics** & \\ Age (years) & 76 [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56;  
The patient cohort was composed of 154 patients that had a median age of 76 years and contained a total of 135/154 (88%) males with a median BSA of 1.8 m\({}^{2}\). Echocardiographic variables included d_LA, LVEF, IVS, and PW having median values of 48 mm, 57%, 18 mm, and 16 mm, respectively.
# MIMO Signal Detection Based on IM-LSTMNet Model
## 3 Experiments and Results of Signal Detection
### The Result of the Signal Detection
#### 3.2.5 IM-LSTMNet Detects MIMO-OFDM Signals under Optimal Conditions  
When the FrFT order P is 1.6, subcarrier number is 64, CP value is 16, and the channel impulse response H is the same at the detection and the training, the IM-LSTMNet model has the best signal detection performance for the MIMO-OFDM system. Under this condition, the BER of MIMO-OFDM signal detection by IM-LSTMNet model under different SNRS is shown in Table 3.  
Figure 18: The BER of the neural network algorithm under different values of H.  
As can be seen from Table 3, when SNR < 5 dB, the bit error rate of the IM-LSTMNet detection of MIMO-OFDM signals is large and decreases slowly, resulting in poor system performance. When SNR > 5 dB, IM-LSTMNet detects MIMO-OFDM signals with small bit error rate and rapid decline, resulting in improved system performance.  
According to the above experiments, the signal detection performance of the IM-LSTMNet model introduced in this paper surpasses not only the traditional ZF and MMSE methods but also outperforms the LSTM and CNN-LSTM neural network approaches.
###### Abstract  
The development of high-speed camera systems and image processing techniques has promoted the use of vision-based methods as a practical alternative for the analysis of non-contact structural dynamic responses. In this study, a deviation extraction method is introduced to obtain deviation signals from structural idealized edge profiles. Given that the deviation temporal variations can reflect the structural vibration characteristics, a method based on singular-value decomposition (SVD) is proposed to extract valuable vibration signals from the matrix composed of deviations from all video frames. However, this method exhibits limitations when handling low-level motions that reflect high-frequency vibration components. Hence, a video acceleration magnification algorithm is employed to enhance low-level deviation variations before the extraction. The enhancement of low-level deviation variations is validated by a light-weight cantilever beam experiment and a noise barrier field test. From the extracted waveforms and their spectrums from the original and magnified videos, subtle deviations of the selected straight-line edge profiles are magnified in the reconstructed videos, and low-level high-frequency vibration signals are successfully enhanced in the final extraction results. Vibration characteristics of the test beam and the noise barrier are then analyzed using signals obtained by the proposed method.  
vision-based measurement; high-speed camera; deviation extraction; low-level dynamic response; video acceleration magnification; singular-value decomposition +
Footnote †: journal: Applied Sciences
# Preliminary Propulsion and Power System Design of a Tandem-Wing Long-Range eVTOL Aircraft
## 5 Results and Discussion of the Power System Design
### Battery Configuration  
Using the nominal voltage and capacitance from Section 5.1 and Equation (19), the total energy per cell was computed to be 18.5 Wh. An analysis of the vehicle's power budget showed that the power required for components other than the propulsion system amounts to approximately 1% of the total power required [3] (Section 9.3). Using this information and Equations (20) and (21), it was found that there are 16,114 cells required for the energy storage for the propulsion system, and 163 cells for other electronic devices, resulting in a total of 16,277 cells. These numbers will increase, since they do not take into account the distribution of cells in series and in parallel.  
The next steps solely focus on the cells for the propulsion system. The required voltage for the motors is 500 V, based on Calnetix [21]. The number of cells in series is based on the required voltage, see Equation (22), and is 136. From Equation (23), it follows that the number of cells in parallel is 119. Due to rounding, this means that the number of battery cells is increased from 16,114 to 16,184.  
Since the Wigeon features 12 propellers (Section 3) and 2 batteries are required per propeller to ensure the aforementioned redundancy, a total of 24 batteries were used. This means that the total number of cells in parallel needs to be a multiple of 24, to ensure that a modular battery design is possible. The number of battery cells in parallel of the wholesystem increased from 119 to 120 to allow for this. This gives 24 batteries, all with 5 cells in parallel.  
The total increase in the number of cells can now be calculated using Equation (25). The number of cells increased by 206, which is an increase of 1.27% over the initial value. There are 16,320 cells required for the propulsive system, and 16,483 in total.
# A Lightweight and Efficient Detection Transformer for Highway Abandoned Objects
## 3 Experimental Results and Performance Analysis  
### Dataset and Implementation Details  
To evaluate the effectiveness of the proposed algorithm, we extracted images containing abandoned objects from roadside surveillance videos captured along specific highway segments in Nanjing, Jiangsu Province, China, and manually annotated these images. We used these data to establish the Highway Abandoned Object Dataset (HAOD), with some sample images illustrated in Figure 7. The dataset contains a total of 4840 images of abandoned objects captured in highway daytime scenes, taken from various angles, lighting conditions, and categories, with each image having a resolution of 1920 \(\times\) 1080. We consolidated all abandoned objects into one category in the dataset. Additionally, to reduce the impact of environmental factors during training and evaluation, we annotated the abandoned objects and labeled additional factors that could potentially influence the detection of abandoned objects, including vehicles, road signs, traffic cones, roadside leaves, shadows, tripods, and birds. These additional annotations enhance the model's capacity to identify abandoned objects within intricate backgrounds, improving its object detection accuracy. The dataset was divided into training, validation, and test sets in an 8:1:1 ratio.  
The experimental hardware platform for this study was an NVIDIA Tesla P100 GPU, with Ubuntu 16.04.6 LTS as the operating system. Python version 3.8.19 was used, and the LE-DETR model was built using the PyTorch framework. The AdamW optimizer was employed, with an initial learning rate set to 0.0001 and weight decay set to 0.0001. For images with an adaptive size of 640 \(\times\) 640, the batch size was set to 4. Data augmentation techniques included random color distortion, expansion, cropping, flipping, and resizing, following [35]. Additionally, all mentioned models employed the train-from-scratch strategy and utilized the more persuasive COCO evaluation metrics.
# Optical Frequency Comb Expansion Using Mutually Injection-Locked Gain-Switched Lasers
## 3 Results and Discussion  
### Expanded OFC Generation Using MIL-GS Lasers  
The continuous wave (CW) optical spectra of the two slave lasers, with an SMSR \(>\)60 dB, is shown in Figure 3a. Figure 3b shows the overlapped spectra of the gain-switched slave 1 (blue) and 2 (red) at an FSR of 6.25 GHz and the common master laser at 1549.6 nm (black). The GS spectra in Figure 3b illustrate the absence of discernible comb lines, indicating that there is no pulse-to-pulse phase coherence in the temporal domain [40]. The external injection from the master laser locks the phase of the successive pulses, resulting in the distinguishable comb lines, as depicted in Figure 3c. The generated MIL-GS OFC 1 and 2 features 12 and 15 comb lines and exhibits an OCNR \(>\)50 dB (within 20 MHz OSA resolution). The larger number of comb tones in OFC 2 is due to the GS laser  
Figure 3: Optical spectra of **(a)** slave lasers in CW state; **(b)** Master and GS slave 1 and 2; **(c)** MIL-GS OFC 1 and 2; **(d)** Expanded OFC (combined MIL-GS OFC 1 and 2).  
Figure 2: Experimental setup of the proposed MIL-GS OFC generation scheme. Here, RF sig.: radio frequency signal source; RF amp: amplifier; CIR: circulator; PS: phase shifter; ODL: optical delay line; PC: polarization controller; PM: phase modulator; \(f_{s}\): free spectral range (FSR); OSA: optical spectrum analyzer.  
2 benefiting from an enhanced modulation response, as the injection wavelength is set to the lower end of its GS spectrum [6]. As mentioned above, injection locking with a common master laser ensures that both GS OFCs are phase and frequency synchronized. Thus, by combining them, a single MIL-GS OFC, with 27 lines and a bandwidth of ~168.75 GHz is generated, as illustrated in Figure 3d. In the following sub-sections, we experimentally demonstrate further comb expansion and the concept of comb densification by employing a phase modulator.
# Data-Driven and Machine-Learning-Based Real-Time Viscosity Measurement Using a Compliant Mechanism
###### Abstract  
In this work, a novel method of viscosity measurement is proposed using a device comprising a compliant mechanism, a vibration source, and a piezoelectric sensor. The vibration source creates linear harmonic vibrations in the compliant mechanism suspended in the liquid, and the acceleration response of the mechanism is measured using the piezoelectric sensor. The vibration source is located in the central mass of the compliant mechanism, which is designed to have the necessary directional stiffness. As the mechanism vibrates, the links in the mechanism undergo damping due to the shearing action of the fluid because of its viscosity. A series of viscosity measurements are carried out with the use of water-glycerol solutions such that the acceleration of the mass is influenced by the fluid's viscosity. During the working of the device, the mechanism is immersed in the liquid whose viscosity is to be measured. The acceleration response of the mass is recorded as time domain data using NI Lab View hardware and software, which are used to train a machine learning model. Later, a regression-based machine learning model is used for the estimation of dynamic viscosity for the given acceleration input. Experiments are performed with the prototype device using the water-glycerol solution within a viscosity ranging from 10 cP to 60 cP. The proposed sensor can be used for in-line measurements or used as a handheld instrument for quick measurements. The machine learning model achieved a high level of accuracy, evidenced by an R-squared value of 0.99, indicating that it explains 99% of the variance in the data.  
machine learning; viscosity prediction; compliant mechanism; regression analysis; NI Lab View +
Footnote †: journal: applied sciences
## 2 Methodology
### Quantum-Behaved Particle Swarm Optimization Algorithm (QPSO)  
In the literature, there exist many variations of PSO, such as quantum-behaved PSO, deep learning driven-PSO, hybrid PSO-Bacterial foraging optimization (BFO) and PSO-Grey Wolf Optimizer (GWO) hybrid approach [32; 33; 34; 35]. PSO variations aimed at providing faster convergence rates, avoiding local maxima traps and parameter selection rules for better performance of the PSO. In this paper, we focus on utilizing the quantum-behaved PSO(QPSO) presented by [PERSON] et al. [36] that has fewer parameters to control and outperforms the original PSO algorithm.  
In classical PSO, the particles' positions and velocities are calculated at each iteration step and updated to diversify the search space and converge towards the optimal solution. Thus, the trajectory of the movement of particles within the search space is deterministic. However, in quantum mechanics, and according to [PERSON]'s uncertainty principle, the velocity and the position of the particle cannot be determined simultaneously [37] and the state of the particle is described by Schrodinger's wave function \(\psi\)(x,t). By solving [PERSON]'s equation to get the probability density function of the particles location in the space, and using Monte Carlo simulation, the position of movement of the particle can be presented as follows:  
\[\left\{\begin{array}{ll}x_{i,j}(t+1)=p_{i}(t)+\beta|Mbest_{j}(t)-x_{i,j}(t) \left|\ln(\frac{1}{u})&if\ k\geq 0.5\\ x_{i,j}(t+1)=p_{i}(t)-\beta|Mbest_{j}(t)-x_{i,j}(t)\left|\ln(\frac{1}{u})&if\ k<0.5 \end{array}\right.\right. \tag{11}\]  
where we have the following:  
_Mbest_ is the Mainstream thought or mean best value, \(x_{i,j}(t+1)\) the position of the \(i\)-th particle in the \(j\)-th dimension of the space, \(u\) and \(k\) are the uniform probability distribution parameters in the range \([0,1]\), \(\beta\) denotes the contraction-expansion coefficient and the \(p_{i}\) as the local attractor point.  
The mean optimal value is described as the mean of each personal greatest of the population and can be evaluated as follows:  
\[Mbest_{j}(t)=\frac{1}{N}\sum_{j=1}^{N}p_{g,j}(t) \tag{12}\]  
In which \(g\) represents the best particle's index in the population. In addition, the local attractor, \(p_{i}\), guarantees the convergence of the algorithm [37] and is defined as follows:  
\[p_{i}(t)=\frac{c_{1}p_{k,i}+c_{2}p_{g,i}}{c_{1}+c_{2}} \tag{13}\]  
where \(p_{k,i}\) and \(p_{g,i}\) represent the _pbest_ and _gbest_, respectively. The pseudocode of the QPSO algorithm is as presented in Algorithm 2.  
```
Step 1: Setting population size and random initialisation of particle positions and velocities. Step 2: Evaluation of particles fitness according to required objective function Step 3: Evaluation of personal best solution of each particle Step 4: Evaluation of global best solution Step 5: Calculating of Mean best (Mbest) of all the _pbest_ of the population Step 6: Position update Step 7: Repeat Steps 2-6 until termination criteria met.
```  
**Algorithm 2** QPSO algorithm
# An RTK UAV-Based Method for Radial Velocity Validation of Weather Radar
## 2 Materials and Methods
### UAV Velocity Reference Calculation and Accuracy Analysis  
The RTK system of the DJI Matrice 300 UAV acquires the real-time position in seconds via GPS, GLONASS, BeiDou, and other satellite navigation systems. The UAV outputs the geodetic coordinates of the target location based on the position and orientation system (POS) data.
# A Hands-On Laboratory for Intelligent Control Courses
## 4 Practical Sequence
### Design of the Fuzzy Controller  
The objective is to design the three stages of a fuzzy controller, fuzzification, rules and defuzzification.  
As shown in Figure 1, there is a desired angular position value \(\theta^{*}\) and the actual value measured by the encoder \(\theta\) is fed back. The error \(E(s)\) is obtained according to Equation (15). Then, \(E(s)\) is the first input variable for the fuzzy controller and its membership functions are defined by Figure 7a, which consists of three triangular functions, an R-function and an L-function, with a range from \(-50\) to \(50\)\(\frac{\pi}{rad}\).  
\[E(s)=\theta^{*}(s)-\theta(s) \tag{15}\]  
For this project, another input variable was selected, which is the weight that is added to the motor shaft. Its membership functions consist of a trapezoidal function, an L-function and an R-function. The range of this variable is from 0 to 30 kg. These are displayed in Figure 7b.  
Finally, for the output variable three triangular functions, an R-function and an L-function are selected; the range is \(-10\) to 10 V. Membership functions for the output variable are exhibited in Figure 7c.  
The rules for fuzzy controlled are defined in Table 6.  
The chosen method for defuzzification is the Center of Area (CoA), which is described in Equation (16). CoA is the center of the area, \(x\) is the value of the linguistic variable and \(x_{min}\) and \(x_{max}\) represent the range of the linguistic variable. In the CoA, the controller calculates the area of the membership functions and within the range of the output variable.  
\[CoA=\frac{\int_{x_{min}^{x_{max}}f(x)*xdx}^{x_{max}}}{\int_{x_{min}^{x_{max}}f( x)dx}^{x_{max}}}=U_{c} \tag{16}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Error/Weight** & **Very Negative (cmn)** & **Negative (en)** & **Zero (ceero)** & **Positive (ep)** & **Very Positive (emp)** \\ \hline Light (pl) & smp & sp & scero & sn & sn \\ Normal (pn) & smp & sp & scero & sn & smn \\ Heavy (pp) & smp & scero & scero & smn & smn \\ \hline \hline \end{tabular}
\end{table}
Table 6: Rules for the inference engine.
## 4 Conclusions and Future Work
### Future Work  
(1) Since the object detection module relies on deep learning algorithms, its performance is to some extent constrained by the size of the dataset. Additionally, when switching to new target objects, the process of recollecting and annotating data remains labor-intensive. Therefore, future work should further explore methods such as adaptive fine-tuning, incremental learning, and transfer learning to enhance the model's performance and generalization capabilities.  
(2) In the process of feature fusion between RGB information and point cloud data, issues such as viewpoint differences, resolution mismatch, and occlusion may arise during data acquisition, affecting the quality of fusion. Therefore, future research should consider incorporating transformer-based feature alignment mechanisms to improve the accuracy and robustness of multi-modal feature fusion.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Object Pose** & **Grasp Attempts** & **Failures** & **Success Rate** \\ \hline Pose1 & 25 & 3 & 88\% \\ Pose2 & 25 & 4 & 84\% \\ Pose3 & 25 & 2 & 92\% \\ Pose4 & 25 & 5 & 80\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results of the grasping experiment.  
(3) In practical grasping tasks, the complex geometry of certain workpiece poses may cause collisions between the gripper and the object, leading to grasp failures. Consequently, future research will focus on optimizing the grasp path to further improve grasp success rate and stability, and we will conduct a quantitative comparison with the state-of-the-art GraspNet framework under the same experimental conditions to comprehensively evaluate the proposed improvements.  
Conceptualization, X.Y. and X.Q.; methodology, X.Y. and X.Q.; software, X.Y.; validation, X.Q. and L.Z.; formal analysis, L.Z.; investigation, J.W.; data curation, J.W.; writing--original draft preparation, X.Q.; writing--review and editing, X.Y. and Y.C.; supervision, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Hubei Province (Youth Program) (2023 AFB381), the Hubei Agricultural Machinery Equipment Shortcomings Tackling Project \"Research, Development, and Promotion of Key Technologies and Equipment for Aquatic Product Processing\" (HBSNYT202221), and the Youth Talent Project of the Science and Technology Research Program of the Hubei Provincial Department of Education (Project Number Q20231412).  
Not applicable.  
The data presented in this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy restrictions.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### RNA Extraction and Sequencing  
Three key fruit development stages (S2, S5, and S7) of 'WCHL' and 'YCHL' pitaya pulps were used for RNA-Seq with three biological repetitions (PRJNA797242). The total RNA was extracted using the RNA Prep Pure Plant Kit (TIANGEN, Beijing, China) according to the manufacturer's instructions. The integrity was assessed using the RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA, USA). A total of 1 ug total RNA of each sample was used as input material for the RNA sample preparations. The first-strand cDNA was synthesized using random hexamer primer and M-MuLV Reverse Transcriptase (RNase H-). The second-strand cDNA was synthesized using DNA Polymerase I and RNase H. The library fragments were purified with AMPure XP system (Beckman Coulter, Beverly, MA, USA) to select cDNA fragments of preferentially 370-420 bp in length. PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers and Index (X) Primer. The library quality was assessed using the Agilent Bioanalyzer 2100 system (Agilent, Santa Clara, CA, USA). The clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v3-cBot-HS (Illumina, San Diego, CA, USA) according to the manufacturer's instructions. After cluster generation, the library preparations were sequenced on an Illumina Novaseq platform and 150 bp paired-end reads were generated.