esa-sceva/satcom-chunk-collection huggingface dataset:
# Acoustic and Flow Aspects of Synthetic Jet Actuators with Chevron Orifices
## 2 Materials and Methods
### Data Reduction  
Velocity is one of the basic parameters used to calculate almost all parameters investigated in SJs ([PERSON] et al., 2018). [PERSON] et al. (2018) showed that centerline velocity is the representative value in the case of axisymmetric SJs. For this reason, only the centerline velocity of SJs is measured in this paper. The following velocities were used ([PERSON] et al., 2018):  
* Local centerline momentum velocity: \[U_{\text{rms,c}}=\sqrt{\frac{1}{T}\int(u_{c}^{2})\text{d}\tau},\] (1)
* Local centerline time-averaged velocity: \[U_{\text{avg,c}}=\frac{1}{T}\int u_{c}\text{d}\tau,\] (2)
* Local centerline maximum velocity: \[U_{\text{max,c}}=\text{max}(u_{c}),\] (3) where \(T\) is the operation period of the loudspeaker membrane [s], and \(u_{c}\) is the instantaneous velocity at the orifice axis [m/s].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Relative Accuracy** & **Absolute Accuracy** \\ \hline Power, \(P\) & \(\pm 2\)\% & \\ Force, \(F\) & & \(\pm 1\) mN \\ Velocity, \(u\) (calibration accuracy) & \(\pm 2\)\% (2.6–49 m/s) & \(\pm 0.1\) m/s (<2.6 m/s) \\ Efficiency, \(\eta\) & & \(\pm 0.2\)\% \\ SPL & & \(\pm 1.4\) dB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Measurement uncertainties.  
Figure 2: Scheme of the measurement stand with all the used equipment (**a**), photo of some parts of the measurement stand during velocity measurements (**b**), and a photo of the actuator during time mean reaction force measurements (**c**).  
Additionally, the centerline synthetic jet turbulence intensity is defined as  
\[Tu=\frac{\sqrt{\frac{1}{T}\int\left(u_{c}-U_{\text{avg},c}\right)^{2}\text{d}\tau} }{U_{\text{avg},c}}, \tag{4}\]  
The energetic efficiency of SJs is calculated by reaction force measurements. This method is described in [27]. The efficiency is calculated as  
\[\eta=\frac{1}{2P}\sqrt{\frac{F^{3}}{A_{0}\cdot\rho}}, \tag{5}\]  
where \(F\) is the time-average reaction force measured by the precision balance [N], \(A_{0}\) is the cross-section of the orifice [m\({}^{2}\)], \(\rho\) is the air density calculated from the ideal gas formula [kg/m\({}^{3}\)], and the real power is calculated as  
\[P=EI\cdot cos(\varphi), \tag{6}\]  
where \(E\). is effective voltage [V], \(I\) is effective current [A] and \(cos(\varphi)\) is the power factor. An impendence measurement is used to designate it:  
\[Z=\frac{E}{I}, \tag{7}\]
# Tree Internal Defected Imaging Using Model-Driven Deep Learning Network
## 3 Experimental Results and Analysis
### Model Settings  
#### 3.2.1 Build Simulation Environment  
The simulated imaging experiments were performed with the help of a simulation environment built in Matlab R2017b. The BP neural network simulation imaging and model-driven deep learning network-based inversion algorithms were mainly performed under the TensorFlow framework in Python 3.6. The computer has a Win10 operating system, Intel(R) Core(TM) i7-8700 CPU @ 3.20 GHz, 16 GB of RAM, and NVIDIA GeForce RTX 2060 display adapter. In the actual standing wood defect model, parameters that affect electromagnetic wave scattering are the dielectric constant of the material, the conductivity information, and the model size. Thus, in the simulation test, the relative permittivity of the model is set to one, and the rest of the relative permittivity is set according to the actual medium information, as shown in Table 2. In the process of tree growth, the relative permittivity information is mainly related to the xylem water content, and not much related to the tree species. Therefore, this paper only considers the relative permittivity distribution inside the xylem affected by the tree water content.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Name** & **Value** & **Parameter Name** & **Value** \\ \hline Domain & 0.32 m \(\times\) 0.32 m & \begin{tabular}{c} Relative permittivity \\ of internal defects \\ \end{tabular} & 20/40/60 \\ Radius of trunk & 0.1 m & \begin{tabular}{c} Impedance of air \\ Number of \\ electromagnetic wave \\ \end{tabular} & 120\(\pi\) \\ Radius of internal defects & 0.01 m/0.02 m & \begin{tabular}{c} electromagnetic wave \\ transmitters \\ \end{tabular} & 12 \\ Frequency & 200 MHz–700 MHz & \
\begin{tabular}{c} electromagnetic wave \\ receivers \\ \end{tabular} & 12 \\ Resolution & 100 \(\times\) 100 &
\begin{tabular}{c} Relative permittivity \\ of xylem \\ \end{tabular} & 1 \\ Relative permittivity of air & 1 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation model parameter settings.  
In the simulation model, white represents air whose relative permittivity is set to 1, and the air impedance value is set to 120\(\pi\). The yellow color is the two-dimensional cross-section of the xylem, which is a circle of 10 cm radius, and its relative permittivity is set to five. The gray part inside the xylem is a circular defect of 2 cm radius. The imaging resolution is 100 \(\times\) 100. To obtain the scattered field data of the solution domain, electromagnetic wave transceivers with the same transmitting and receiving frequencies are selected for the detection, and the imaging frequencies are from 200 MHz to 700 MHz. A total of 32 groups of electromagnetic wave transmitters and 32 groups of electromagnetic wave receivers are evenly distributed on the circular arc around the solution domain.
# Quality Assessment of Honey Powders Obtained by High- and Low-Temperature Spray Drying
## 3 Results and Discussion
### Aroma Compounds  
Table 3 presents the results of aroma compounds in reconstituted honey samples of feed solutions. Powders were reconstituted in water to the concentration of the feed solutions. Analyzed samples were characterized by the occurrence of different volatiles in the aroma. The overall count of different aroma compounds of rapeseed powders and solution was 101 and in honeybee honey was 109 (Table 3). \begin{table}
\begin{tabular}{c c c c c} \hline
**Group of Compounds** & **Compounds** & **RH-SD mg/100 g** & **RH-DASD** & **Feed Solution** \\  & **Solids** & **mg/100 g Solids** & **mg/100 g Solids** & **mg/100 g Solids** \\ \hline
4-Hydroxybutanoic acid & 0.25 \(\pm\) 0.08 & nd & nd \\ \hline
3-(2-amino-1H-imidazol-5-yl)prop-2-enoic acid & nd & 2.81 \(\pm\) 0.43 & nd \\ \hline (E)-hex-2-enoic acid & nd & 0.46 \(\pm\) 0.17 & nd \\ \hline
2-acetyloxyacetic acid & nd & 0.56 \(\pm\) 0.07 & nd \\ \hline
3-(Acetylsulfanyl)-2-methylpropanoic acid & nd & 0.61 \(\pm\) 0.10 & nd \\ \hline
4-oxopentanoic acid & nd & 2.90 \(\pm\) 0.43 & nd \\ \hline Acetic acid & nd & 8.11 \(\pm\) 0.87 b  & 2.24 \(\pm\) 0.23 a  \\ \hline Benzoic acid & nd & 0.68 \(\pm\) 0.14 b  & 0.04 \(\pm\) 0.02 a  \\ \hline Hexadecanoic acid & nd & 1.77 \(\pm\) 1.07 b  & 0.15 \(\pm\) 0.02 a  \\ \hline Nonanoic acid & nd & 0.71 \(\pm\) 0.10 b  & 0.25 \(\pm\) 0.15 a  \\ \hline Octanoic acid & nd & 0.83 \(\pm\) 0.07 b  & 0.20 \(\pm\) 0.02 a  \\ \hline Heptanoic acid & nd & nd & 0.06 \(\pm\) 0.01 a  \\ \hline Decanoic acid & nd & nd & 0.06 \(\pm\) 0.02 a  \\ \hline Tetradecanoic acid & nd & nd & 0.05 \(\pm\) 0.04 a  \\ \hline N-Hexadecanoic acid & nd & nd & 0.45 \(\pm\) 0.13 a  \\ \hline
9-Octadecenoic acid & nd & nd & 0.71 \(\pm\) 0.31 a  \\ \hline Oleic acid & nd & nd & 1.53 \(\pm\) 0.24 a  \\ \hline Formic acid & nd & 4.23 \(\pm\) 0.24 & nd \\ \hline
3-methylbutanoic acid & nd & nd & 0.19 \(\pm\) 0.05 a  \\ \hline
**Total acids** & **0.25 \(\pm\) 0.08 a**  & **23.68 \(\pm\) 0.22 c**  & **6.10 \(\pm\) 2.64 b**  \\ \hline \end{tabular}
\end{table} 
Table 3: Aroma compounds content in rapeseed and honeybee honey powders and solutions: SD—traditional spray drying, DASD—dehumidified air spray drying, RH—rapeseed honey, HH—honeydew honey. a–c: differences between mean values followed by different letters in rows were statistically significant (\(p<0.05\)).
# The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends
## 7 Challenges and Future Research Trends
### Global Resource Management-Related Challenges  
Resource distribution is critical in serving productivity and reducing cost and can be divided into two groups, the global allocation of resources and local allocation. Global concerns concentrate on the expenditure spent on the global resources of time, energy, and equipment. In addition, the maximum global productivity can be deployed by various equipment in the Internet of everything (IoE), such as edge computers, cloud servers, and UAVs. Under digital media transmission in IoE, global efficiency can be increased by networking algorithms and video coding. Energy and power are allocated for every role based on committed performance. For instance, in a smart building scenario (such as in a local network), an efficient data collection algorithm is good for increasing the user's data rate. In addition, for the flying terminals, the energy management system is essential for making use of limited energy. This is the concept of allocating resources at various nodes [81].  
In fact, a challenge for UAV applications is to preserve efficiency while facing cost issues. First, developing protocols for the provisioning of network services based on information rate and requirements of computation is a challenge. The problems to be addressed are for systems that are clustered, use of cloud resources, and autonomous services. In addition, the most popular economic activity is \"resource allocation\" since it includes competing interests of individuals and companies or services to receive particular resources. The next challenge is discovering reliable and cost-effective ways to assign consumer demands. Another difficulty in resource utilization centers on determining how best to specify the priorities and paths to the minimization of activity energy expenditure. Moreover, resource mapping aims to offer an equal amount of services to buyers and suppliers. Resource mapping may face problems such as mapping the physical components and the rational distribution of resources to fulfill the requirement, which is often hindered by physical limitations and obstacles. Designing algorithms that can use generic methods to quickly obtain a mapping process fast is critical. The application's compatibility with the UAV platform needs to be checked by installing corresponding hardware prerequisites. Yet another difficulty to be resolved would be to build models that can correctly measure the performance of multi-core CPUs, PC storage medium, communication channels, and information center data stores. In addition, this consideration may be a problem when mapping resources [79].
# Digital Signal Processing of the Inharmonic Complex Tone
## 3 The Developed Application Details
### Submodule MuPL_A  
The third and final submodule, MuPL_A, is designed as a tool for the simple manipulation of the submodule channels (Figure 7). During the initialization of this submodule, time envelopes of all channels are fitted, and parameters \(A_{n1}\)=\(A_{n5}\), as described in Section 2.3, are extracted. The user can adjust all parameters based on the inspection of the result using time and frequency representations of the original and modified content of the channel signal, as well as by listening to the original and modified signals. Currently, several options for modification are supported:  
* Amplitude scaling of the signal (including total mute);
* Replacing the extracted channel signal with \[A_{n}^{fit}[k]\cos\left(\frac{2\pi}{f_{s}}f_{n}k+\varphi_{n}\right)+s_{-}n\cdot filt _{noise_{n}}[k]\] (19)
* where \(A_{n}[k]\) is the envelope obtained by the fitting of the channel extracted envelope; \(f_{n}\) is the partials frequency, estimated by the frequency position of maximum in DFT of the channel signal; \(fill\_noise[k]\) are samples of the filtered Gaussian noise; and \(s\_n\) is the scaling factor (it can be zero, i.e., the noise component can be omitted). The filtered noise is the output signal from the same filter bank used for the signal processing, with the Gaussian random array as the input signal;
* Replacing the extracted channel signal with the samples of filtered Gaussian noise, as explained in relation to (19).  
The input signal can be perfectly reconstructed by simply adding all subchannels, with the exception of a small unavoidable error resulting from non-ideal zero-phase filtering, as explained in Section 2.1. The sum can include a modified signal instead of the original channel signal. As a result, a slightly modified overall signal is produced. This basic  
Figure 7: Submodule MuPL_A window, with an initial view presented. The first channel (displayed in the figure) corresponds to the first partial. The time and frequency content of the signal is plotted, along with estimated and fitted time envelopes. All relevant extracted parameters are displayed for the current channel. The graph on the left shows the signal in time (green color), the extracted envelope of the signal (solid black line) and the repaired envelope (dashed black line). Color coding is used to indicate the position of the currently selected channel in the entire spectrum (lower channels are marked in green, higher in red). Graph on the bottom right: the spectrum of the extracted channel is shown in green, and the spectrum of the signal from the channel with the corrected envelope is shown in dashed black.  
signal synthesis can be used for a preliminary investigation of the impact of specific signal parameters or components on the perceptual quality of the signal, for example, in verifying stimulus synthesis for listening tests.
# Modeling Soil Temperature with Fuzzy Logic and Supervised Learning Methods
## 2 Materials and Methods  
### Site Description and Data  
The province of Tokat (40\({}^{\circ}\)55\({}^{\prime}\) N latitude and 37\({}^{\circ}\)39\({}^{\prime}\) E longitude) is situated in the Black Sea region of Turkiye (Figure 1), covering an area of 9958 km\({}^{2}\) with an average elevation of 623 m above sea level. Meteorological data for the province were obtained from the General Directorate of State Meteorological Services. The dataset includes daily air temperature measured at a height of 2 m, as well as soil temperature recorded at depths of 10, 20, 50, and 100 cm. These observations span the period from 2016 to 2024. For model development, data from 2016 to 2021 were used for training, while data from 2022 to 2024 were reserved for testing.  
Figure 1: Location of Tokat province on Turkiye.
# An Improved YOLOv8 Model for Strip Steel Surface Defect Detection
## 2 Improved YOLOv8 Detection Principle
### C2f-BAM Attention Mechanisms  
Due to the large difference in the target shape and size of the defects in the steel strip surface data set, it is difficult for the model to accurately identify and locate the defects, so the BAM attention mechanism is introduced. This mechanism improves the model's ability to focus on important features in complex environments, thus enhancing object detection performance. [PERSON] et al. [39] effectively extracted the shape, contour, and structural features of steel strip defects by incorporating the boundary-aware module (BAM), which also accurately localized the defects. [PERSON] et al. [40] added a global attention mechanism module to C2f, focusing on defect areas and suppressing irrelevant regions, dynamically adjusting feature responses to more accurately recognize image features. These studies provide strong support for the effectiveness of the improvements to C2f in enhancing detection performance by increasing the focus on relevant features.  
The structure of the BAM is shown in Figure 8. For the input feature map \(F\), the channel dimension features are generated by using global average pooling in the channel attention module (CA), and then the input channel weights are learned through two fully connected layers \(FC\), and finally, the \(C\times 1\times 1\) channel attention weights \(M_{c}(F)\) are obtained.  
\[M_{c}(F)=BN(W_{1}(W_{0}AvgPool(F)+b_{0})+b_{1}) \tag{5}\]  
The spatial attention module (SA) uses \(1\times 1\) convolution for dimensionality reduction and then learns the spatial weight of the feature map by repeated dilated convolution operations. Finally, the feature weight is reduced to 1 by \(1\times 1\) convolution, and the spatial attention weight \(M_{s}(F)\):  
\[M_{s}(F)=BN(f_{3}^{1\times 1}(f_{2}^{3\times 3}(f_{1}^{3\times 3}(f_{0}^{1 \times 1}(F))))) \tag{6}\]  
The spatial and channel attention are summed element-by-element, and the original feature map is weighted by using the activation function and the final comprehensive attention weight to obtain \(F^{\prime}\).  
\[F^{\prime}=F+F\otimes(\sigma(M_{c}(F)+M_{s}(F)))) \tag{7}\]  
The neck attention module BAM is introduced at the bottleneck of C2f architecture to enhance the feature representation ability of the model. Through reasonable configuration and experimental verification, a good balance between performance and efficiency is achieved. Adding BAM directly to the bottleneck module can make full use of the power  
Figure 8: Structure diagram of BAM.  
of the attention mechanism while maintaining the advantages of the original structure to improve the performance of the model in various tasks. The C2f-BAM structure diagram is shown in Figure 9.
## 5 Conclusions  
This paper represents an in-depth study on the dynamics modeling and theoretical study of the two-axis four-gimbal coarse-fine composite electro-optical pod. Our conclusions are as follows.  
* In the UAV electro-optical pod of the two-axis four-gimbal, the characteristics of the coarse-fine composite structure and the complexity of dynamics modeling affect the entire system's high-precision control performance. The core goal of this paper is solve the high precision control of two-axis four-gimbal electro-optical pod through dynamic modeling and theoretical study. FEA and theoretical analysis of the stress and deflection of the key structure component was used to design the structure. The gimbal structure adopts 7075-t3510 aluminum alloy, which is an aerospace material that meets the requirements of an ultralight electro-optical pod weighing less than 1 kg.
* According to the Euler rigid body dynamics model, the transmission path and kinematics coupling compensation matrix for the two-axis four-gimbal are obtained. The coarse-fine composite drive correction equation of the inner-outer gimbals is derived to solve the pre-selection and check problem of the coarse-fine motors under high-precision control.
* The modeling method is substituted into the DOB disturbance suppression experiment, which can monitor and compensate for the motion coupling between gimbal structures in real time. Our results show that the disturbance suppression impact of the DOB method with dynamics model is up to 90% better than PID and 25% better than traditional DOB.
* This manuscript is based on the dynamics modeling and theoretical study of the two-axis four-gimbal coarse-fine composite UAV electro-optical pod. This manuscript is valuable for all researchers interested in the coarse-fine composite, two-axis four-gimbal structures, and ultralight electro-optical pods.  
Figure 16: Comparison diagram of disturbance suppression difference output of outer roll gimbal A. (**a**) The output deviation of traditional DOB disturbance suppression; (**b**) the output deviation of improved DOB disturbance suppression.  
**Author Contributions:** [PERSON] designed the Euler rigid dynamics model and high precision macro-micro composite DOB control algorithm and carried out experimental research on the effect of the modeling and algorithm. [PERSON] and [PERSON] guided the research and proposed the ideas and revisions of the paper. [PERSON] provide help with control and simulation. [PERSON] provide structure model. [PERSON] and [PERSON] revised the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The author would like to thank all the teachers and colleagues who provided inspirations and equipment in the experiment. The author would like to thank all the anonymous reviewers for their meticulous comments and helpful suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Related Work
Overall, although existing methods for tomato ripeness detection have made significant progress in terms of accuracy and effectiveness, many researchers and engineers have successfully applied various deep learning techniques and image processing algorithms to improve the accuracy of detection. However, these methods still face some challenges in practical applications, especially in terms of real-time performance, computational resource consumption, and adaptation to complex environments. For example, in highly dynamic agricultural environments, factors such as climate change, differences in lighting conditions, and background clutter can affect the performance of the detection system. In addition, most of the existing methods have a high demand for computational resources, a problem that is particularly prominent in mobile devices or edge computing environments, limiting the widespread application of these techniques. To address these challenges, this study proposes a new detection method by introducing Distribution Shifting Convolution [37] and the Swin Transformer module. The main advantage of using Distribution ShiftingConvolution is its ability to effectively reduce the computational complexity, thus allowing the model to be deployed on resource-constrained devices more easily. Meanwhile, the hierarchical self-attention mechanism introduced by the Swin Transformer module enables the model to better capture the global information in the image, which is important for improving the accuracy of detection. This combination not only improves the efficiency of detection but also enhances the adaptability of the model in variable and complex environments, showing good application prospects.