esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Analysis
### Experimental Results
#### 3.4.4 Comparison with the Experimental Results of Different Networks  
Considering that there are few weakly supervised video instance segmentation networks at present, this paper chooses the fully supervised networks CrossVIS, YolactEdge, and ST-Mask to compare with MLGW-VIS, respectively, and the experiments are carried out under the same equipment and environment. The experimental results of each network in Table 4 show that the average accuracy of MLGW-VIS reaches 37.9%, which is 3% higher than that of the fully supervised network YolactEdge, but the segmentation speed is reduced by 9.8 FPS, which is 5.7% higher than that of the benchmark weakly supervised network CWVIS. Compared with the fully supervised network, the average accuracy of STMask and CrossVIS is only 2.1% and 2.7%respectively. MLGW-VIS significantly reduces the cost of dataset labeling compared with the fully supervised network and can achieve real-time segmentation speed of 34.6 FPS on a graphics card 2080 Ti. The experimental results show that the segmentation accuracy of the weakly supervised video instance segmentation method is comparable to that of some current mainstream fully supervised networks, which further proves the effectiveness and feasibility of the MLGW-VIS network. The visual output results of CWVIS and our method are provided in Figure 7, where we can see the significant improvement of our designs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **AP** & **AP-50** & **AP-75** & **AR-1** & **AR-10** & **FPS** \\ \hline YolactEdge [20] & 34.9 & 58.8 & 36.6 & - & - & 24.4 \\ STMask [14] & 40.0 & 62.7 & 42.9 & 42.1 & 45.5 & 22.7 \\ CrossVIS [26] & 40.6 & 65.7 & 42.2 & 43.2 & 46.4 & 15.9 \\ FlowIRN [30] & 17.4 & 37.0 & 11.0 & 10.3 & 19.6 & 10.1 \\ BoxInst [32] & 21.2 & 45.2 & 17.7 & - & - & 23.0 \\ CWVIS & 32.2 & 57.6 & 32.4 & 34.9 & 38.7 & 16.4 \\ MLGW-VIS & 37.9 & 68.9 & 39.7 & 38.9 & 43.3 & 14.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of experimental results of different networks.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **AP** & **AP-50** & **AP-75** & **AR-1** & **AR-10** & **FPS** \\ \hline CWVIS & 32.2 & 57.6 & 32.4 & 34.9 & 38.7 & 16.4 \\ CWVIS + MSL-FPN & 36.6 & 68.8 & 35.5 & 37.5 & 41.7 & 15.9 \\ CWVIS + GWM & 36.2 & 65.7 & 35.4 & 37.5 & 41.1 & 15.9 \\ CWVIS + MSL-FPN + GWM & 37.9 & 68.9 & 39.7 & 38.9 & 43.3 & 14.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The ablation results of this method are obtained.
## 5 Beyond Standard SDS
### CSDS Synchronicity and Context Sensitivity  
A further complication introduced by the decoupling of agent populations in CSDS is that of sequencing the phases and selecting appropriate population sizes. Generally, in other variants of SDS, only one population is maintained, hence the test and diffuse phases are simply executed in order. Applied to CSDS, this can be thought of as synchronous CSDS, in the sense that each population iterates through its cycles, in turn; perhaps, the model population iterates first, followed by the data population.  
Asynchronous CSDS, however, proceeds by allowing each agent population to act in parallel; implemented here, this equates to interleaving the iteration of each population; for example, the model population selects one agent, forms a complete hypothesis with a data agent and performs the test phase; following this, a data agent will do so similarly; next, the model agent will perform diffusion, followed by the data agent; finally, the relate phase is performed in a similar manner. It was found experimentally that asynchronous operation yields the best performance.  
As with DDSDS, the population size chosen is critical. Interestingly, in CSDS, it is possible to vary the size of both the model populations and the data populations. Increasing the size of the model population leads to a wider variety of hypotheses to test and, thus, more SVD calculations to perform, while increasing the data agent population leads to a larger number of residual calculations. Residual calculation is far less computationally expensive than SVD calculation, though SDS tends to perform more of them. Generally, the convergence of the system as a whole is determined by the convergence properties of the model population.
## 4 Experimental Evaluation
### SI Cancellation by Analog Circuit  
The spectrum analyzer was also utilized to evaluate the performance of the SI cancellation performance of the proposed AC prototype. The results are shown in Figure 11. From the values shown in Figure 11, it can be observed that the noise floor is also around \(-110\) dBm. Additionally, the marker 2 in Figure 11 marks the SI power before AC, while marker 3 marks the SI signal power after analog SI cancellation in AC. After about 2.5 dB power loss due to cables and connectors between antenna and AC board, the value measured by marker 2 is \(-46.4\) dBm, while the value marked by marker 3 is \(-68.4\) dBm. From the results, its can be known that the AC can suppress SI signal power by 22.0 dB. The experimental results indicate that a substantial attenuation of the SI power can be provided by the SI cancellation functionality in the AC board, which validates the effectiveness of the AC prototype design.
###### Abstract  
Traditional irrigation techniques usually cause the wasting of water resources. In addition, crops that are located in rural areas require water pumps that are powered by environmentally unfriendly fossil fuels. This research proposes a smart irrigation system energized by a microgrid. The proposal includes two stages: the first generates the daily irrigation profile based on an expert system for the adequate use of the water. Then, considering the irrigation profile, the power required for the water pump is measured--the optimal daily profile of electricity demand is determined in the second stage. The energy system is a microgrid composed of solar energy, a battery energy storage system (BESS) and a diesel generator. The microgrid is managed by an energy management system (EMS) that is based on model predictive control (MPC). The system selects the optimal start-up time of the water pump considering the technical aspects of irrigation and of the microgrid. The proposed methodology is validated by a simulation with real data from an alfalfa crop in an area of Ecuador. The results show that the smart irrigation proposed considers technical aspects that benefit the growth of the crops being studied and also avoids the waste of water.  
model predictive control; energy management system; renewable energy; smart irrigation; agriculture 4.0
# Remote Radio Frequency Sensing Based on 5G New Radio Positioning Reference Signals
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} NR & New Radio \\ PRS & Positioning Reference Signal \\ OFDM & Orthogonal Frequency Division Multiplexing \\ ISAC & Integrated Sensing And Communication \\ FFT & Fast Fourier Transform \\ IFFT & Inverse Fast Fourier Transform \\ DFT & Discrete Fourier Transform \\ CAF & Cross-Ambiguity Function \\ CIR & Channel Impulse Response \\ CFR & Channel Frequency Response \\ FDD & Frequency Division Duplex Mode \\ DDM & Delay-Doppler Matrix \\ RVM & Range-Velocity Matrix \\ AWGN & Additive White Gaussian Noise \\ SNR & Signal-to-Noise Ratio \\ PNFR & Peak-to-Noise-Floor Ratio \\ \(f^{(D)}\) & Doppler Frequency Shift \\ \(x_{TX}\) & Transmitted signal \\ \(y_{RX}\) & Received signal \\ \(y_{p}\) & Reflected Signal from path \(p\) \\ RE & Resource Element \\ RB & Resource Block \\ PRB & Physical Resource Block \\ SCS & Subcarrier Spacing \\ \(\mu\) & Numerology \\ \(N_{Symbols}^{Slot}\) & Number of OFDM symbols per slot \\ \(N_{Slots}^{Subframe,\mu}\) & Number of slots per subframe with numerology \(\mu\) \\ \(N_{Slots}^{frame,\mu}\) & Number of slots per frame with numerology \(\mu\) \\ \(N_{cp}\) & Length of the 5G NR longer cyclic prefix \\ \(N_{ pilots}^{symbol}\) & Number of PRS pilots in one OFDM symbol \\ \(N_{ pilots}^{sc}\) & Number of all PRS pilots on a subcarrier \\ \end{tabular}
# Inf-OSRGAN: Optimized Blind Super-Resolution GAN for Infrared Images
## 5 Conclusions  
To overcome the constraints of current infrared image super-resolution algorithms, this paper proposed an optimized method for super-resolution in the infrared image domain. Firstly, we constructed a high-quality infrared image dataset enriched with detailed textures, serving as the basis for evaluating infrared image super-resolution models. In terms of degradation, we incorporated a gate mechanism and random shuffle strategy to encompass a broad spectrum of realistic degradation encountered in real-world situations. Furthermore, in the training process of the Inf-OSRGAN network, we integrated a spatially correlative loss function to measure feature correlations, to ensure the preservation of structural consistency and detailed information, thereby enhancing both the accuracy and perceptual fidelity of the reconstructed images.  
We validated the proposed method on the constructed high-resolution test set and various publicly available low-resolution infrared image datasets. Our method achieved outstanding performance on both the high-resolution test set and a diverse array of publicly available datasets. The experimental results demonstrated the superior capabilities of our method for detail restoration, with a particular emphasis on the recovery of nuanced textures. Specifically, these experiments substantiated the method's reliability and practicality, showing its versatility across various scenes and scenarios, as well as its robust generalization across images of varying resolutions. The findings of this study offer a potent approach to infrared image enhancement  
**Author Contributions:** Conceptualization, Z.X. and C.K.; methodology, Z.X.; software, J.G. and X.W.; validation, Z.X., J.G., X.W. and C.K.; formal analysis, Z.X., J.G. and X.W.; investigation, C.K.; resources, Z.X.; data curation, Z.X.; writing--original draft preparation, J.G. and X.W.; writing--review and editing, Z.X. and C.K.; supervision, C.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Shandong Provincial Natural Science Foundation, grant number ZR2022 QF101.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 The Model Description and Data Preprocessing
### Parkinson's Disease Detection  
#### 3.2.1 Voice Data Preprocessing and Recurrence Plot Creation  
Here, we explain how to preprocess voice data and create recurrence plots. The voice data used in our experiments are recordings of the voice/a/ including men and women for about 4 to 10 s. Regardless of the recording, all voice data are converted to monaural, 16,000 Hz, and then, processing to create RPs is started, because voice data are recorded in various environments. Additionally, all voice data are quantized in 16 bits.  
The preprocessing of voice data before creating RPs is as follows:  
1. Delete the silent section before and after voice data: In order to avoid the influence of voice volume, all voice data are normalized so that the maximum amplitude is the expressible maximum value, and the sections of \(-40\) dB or less are deleted only from before and after the sound.
2. Furthermore, delete the first and last of the above voice data for 0.1 s: This was because the first and last sounds after deleting the silent sections were often unstable. Next, the procedure of creating recurrence plots is as follows:  
1. Divide the above preprocessed voice data into 0.01 s sections.
2. Immediately before creating the recurrence plot, normalize the sound of 0.01 s section so that it becomes [\(-1\), \(1\)], and then plot points with a distance smaller than the 35 th percentile in 0.01 s section. The reason for using the 35 th percentile is that the accuracy was highest when the 35 th percentile was used in the experiments, which is described later.
3. Compress the generated black-and-white RP with 160 \(\times\) 160 image size to 20 \(\times\) 20 using bilinear interpolation. This may deteriorate the accuracy, but it is carried out in consideration of memory efficiency of Video Random Access Memory (VRAM) when inputting to the neural network.
4. Repeat above 1-3 until all divided voice data become recurrence plots. However, if a fraction less than 0.01 s appears in the last section, it will be rounded down.  
Figure 4: A layer of RNN-SH (cited from [10]). The smaller scalar value \(a\) is, the larger the hidden state and the gradient are after passing through the gate, and the larger \(a\) is, the smaller they are. This \(a\) controls the amount of retained information and the degree of vanishing gradient. The biases are omitted here.
# Routing Design for Pipe System Considering Natural Frequency
## 3 Adaptive Decomposition of Routing Space  
The routing space encompasses various elements such as obstacles, pipes, and accessories, each with complex shapes that are challenging to accurately describe. To simplify the representation of obstacles, some researchers have approximated them as rectangular cuboids and decomposed the routing space into uniformly sized grids. Traditional cell decomposition methods encounter difficulties when dealing with the different diameters of pipes, as commonly found in aero engines. Whenever the diameter changes, the traditional methods require routing space re-decomposition, leading to time-consuming algorithms. To overcome these challenges, this paper proposes an adaptive decomposition method for the routing space. In this section, we provide a detailed introduction to the adaptive decomposition method.
The above studies illustrated that obtaining spectra of samples in a larger wavelength range by integrating spectrometers of different wavelength ranges improves the detection accuracy and the cost effectiveness of the instrumentation. In this respect, real-time detection of tea polyphenols in fresh tea leaves is crucial for tea bush breeding and tea leaf quality monitoring. Herein, our study focused on fresh tea leaves of different tea varieties and established a tea polyphenol detection model based on the fusion of Vis/short-wave and long-wave NIR spectra by integrating spectrometers of different wavelength ranges. Furthermore, the real-time quantitative detection of tea polyphenol content in fresh tea leaves was performed. The specific objectives were to (1) build a portable device incorporating two spectrometers and fuse the spectral data acquired from two spectrometers into one spectrum, (2) develop prediction models for tea polyphenol content based on the individual and combined spectral regions, and (3) extract the sensitive wavelength of tea polyphenol content.
# Payment Maintenance Decision Making Based on Optimization Models
## 5 Discussion
### Extension of the Multiobjective Model  
This section focuses on extension of the multiobjective model. Considering different road grades, expressways have a higher maintenance priority than main roads. Moreover, main roads usually have a higher maintenance priority than branch roads. The MBMMQ model was extended accordingly, as follows:  
\[maxZ_{1}=\sum\
olimits_{i=1}^{n}\left(PCI_{i}^{\prime}-PCI_{i}\right)w_{i}x_{i} \tag{10}\]  
\[minZ_{2}=\sum\
olimits_{i=1}^{n}w_{i}x_{i}Area_{i}\cdot P \tag{11}\]  
Here, \(w_{i}\) denotes the grade of road \(i\), which is set to 1.5, 1, or 0.5 when road \(i\) is an expressway, a main road, or a branch road, respectively. Based on this revised model, the effects of the different road grades and maintenance schemes were further determined, as shown in Figure 4. Compared with the original solution, the maintenance costs and quality are slightly different, which was primarily due to different road PCI values and maintenance areas between expressways and branch roads. These results indicated that the revised model is suitable for multiple road grades.  
Because of constraints on pavement maintenance, a road may be only partially maintained, and its PCI may be lower 100 after maintenance. \(PCI_{i}^{\prime}\) could replace \(x_{i}\) as a decision-making variable, which represents the PCI of the road after maintenance. Based on the MBMMQ model, the third MOO model is proposed as follows.  
\[maxZ_{1}=\sum\
olimits_{i=1}^{n}\left(PCI_{i}^{\prime}-PCI_{i}\right) \tag{12}\]  
\[minZ_{2}=\sum\
olimits_{i=1}^{n}\frac{Area_{i}\cdot P\cdot PCI_{i}^{\prime}}{ PCI_{i}} \tag{13}\]  
\[s.t.\ PCI_{i}\leq PCI_{i}^{\prime}\leq 100 \tag{14}\]  
In this model, \(PCI_{i}^{\prime}\) is selected as the variable, which can vary from \(PCI_{i}\) to 100. The constants \(PCI_{i}\), \(Area_{i}\), and \(P\) are the same as those in Equation (2). Therefore, when calculating the maintenance cost for a specific road, \(PCI_{i}^{\prime}/PCI_{i}\) should be incorporatedinto Equation (13) to consider the effect of PCI improvement on the maintenance area. This model is suitable to maintain quantitatively each selected road and can be solved by NSGA-II.
## 1 Introduction
When lipophilic bioactives are introduced in the lipid phase, the encapsulation efficiency and loading capacity (LC) of these bioactives in SLNs are also affected by their solubility in the lipids and interactions between bioactives and lipid compounds. Despite these versatile advantages, SLNs also exhibit a few disadvantages or limitations, e.g., low LC of drugs or bioactives, and drug expulsion due to the occurrence of polymorphic transition upon storage [12; 19]. To overcome these limitations, a novel kind of lipid nanoparticle (referred to as nanostructural lipid carriers (NLCs)) was proposed and developed nearly two decades ago. Instead of using solid lipids as the core in SLNs, a blend of solid and liquid (unsaturated fatty acids) lipids are applied to formulate NLCs that remain in solid state. The occurrence of imperfect crystallization in the lipid core of NLCs (as illustrated in Figure 1) allows a larger space between fatty acid chains, to facilitate the incorporation of drugs or bioactives, and to alleviate expulsion during polymorphic transitions [19]. Thus, NLCs, as delivery systems for bioactives, exhibit several advantages over SLNs, such as enhanced LC and encapsulation efficiency, better physical stability (e.g., minimized bioactive expulsion upon storage), controlled release, and improved bioavailability of bioactives [27; 28]. This article aimed to provide a systematic review of the usage of food-grade SLNs and NLCs as nanovehicles for hydrophobic (poorly water-soluble) or lipophilic (oil-soluble) nutraceuticalids (and bioactive compounds). First, the structural compositions (lipids, emulsifiers, or co-emulsifiers) and preparation methods of such lipid-based nanoparticles were summarized. Next, some key issues on the usage of such nanoparticles as oral nanovehicles for hydrophobic bioactives, including incorporation and release of bioactives, oxidative stability, lipid digestion and bioactive solubilization, bioactive absorption, and intestinal transport, were critically discussed. Then, the utilization of SLNs and NLCs as nanovehicles for encapsulation and delivery of liposoluble or poorly soluble nutraceuticalids and bioactives (e.g., carotenoids, fat-soluble vitamins, omega-3 polyunsaturated fatty acids, essential oils, curcuminoids, polyphenols, phytosterols, or phytostanols) was comprehensively reviewed. The encapsulation performance, stability, bioaccessibility/bioavailability, and bioactivities of curcuminoids (and curcumin in particular) in such lipid nanoparticles were highlighted. Lastly, strategies to improve the performance of such nanoparticles as nanovehicles for nutraceuticalids were proposed, and future research trends and potential applications in food formulations were discussed.
# Blade Shape Optimization and Analysis of a Propeller for VTOL Based on an Inverse Method
## 2 Modeling
### Blade Element Momentum Theory  
#### 2.2.1 Theory and Numerical Solution  
The BEMT combines the blade element theory (BET) and momentum theory (MT) to solve the axial- and rotational-induced velocities at the propeller disk location. BEMT was first proposed by [PERSON] in 1926 [27]. A significant number of studies have validated the effectiveness of the BEMT for calculating the performance of a propeller. Figure 2. shows the velocity and force acting on the blade element. Here, \(dL\) and \(dD\) are the lift and drag for the blade element, \(dT\) and \(d\tau\) are the axial thrust and tangential force of the blade and \(\theta\), \(\alpha\), \(\beta\) and \(\phi\) are the twist angle, angle of attack, interference angle and inflow angle, respectively. \(V_{0}\) is the far-field velocity, \(v_{a}\) and \(v_{t}\) are the axial- and tangential-induced velocities at the disk, \(W_{1}\) is the resultant flow velocity and \(n_{\rm s}\) is the revolutionary speed in the units of rps.  
The equations to calculate the axial thrust and tangential force combining the BET and MT considering the Prandtl modification are expressed as:  
\[\left\{\begin{array}{l}{dT=\underbrace{2\pi\rho dr|V_{0}+v_{a}|(2v_{a})F} _{MT}=\underbrace{\frac{1}{2}\rho N_{b}[|V_{0}+v_{a}|/\sin\varphi]^{2}cdr[C_{L }(\theta-\varphi)\cos\varphi\text{-}C_{D}(\theta-\varphi)\sin\varphi]}_{BET}}\\ {d\tau=\underbrace{2\pi\rho dr|V_{0}+v_{a}|(2v_{t})F}_{MT}=\underbrace{\frac{1 }{2}\rho N_{b}[|V_{0}+v_{a}|/\sin\varphi]^{2}cdr[C_{L}(\theta-\varphi)\sin \varphi+C_{D}(\theta-\varphi)\cos\varphi]}_{BET}}\\ {\varphi=\arctan\frac{|V_{0}+v_{a}|}{2\pi n_{b}r-v_{t}}}\end{array}\right. \tag{1}\]  
Figure 1: Typical mission profile for VTOL aircraft.  
Figure 2: The velocity and force acting on the blade element.  
Here, the Prandtl modification factor \(F\) is calculated by:  
\[\left\{\begin{array}{l}F=\frac{2}{\gamma_{t}}\arccos(e^{-f})\\ f=\frac{N_{b}}{2}\frac{R-r}{r\sin\varphi_{t}}\end{array}\right. \tag{2}\]  
where \(\varphi_{t}\) is the actual inflow angle at the blade tip. Equation (1) is a non-linear transcendental equation group; [PERSON] [28] gave a general gradient-based iterative method using interference \(\beta\) as the unknown variable to solve. However, the initial guess of \(\beta\) has a significant influence on the solution. An inappropriate initial guess will cause an incorrect solution without a physical meaning and will result in the first-order discontinuity of the interference angle distribution along the radius, especially for low advance ratio conditions. A smoothing modification to the \(\beta\) distribution to ensure first-order continuity was added to the gradient-based iterative procedure to solve Equation group (1) and its effectiveness is shown in Figure 3.